PREDICTIVE CONTROL APPARATUS

Abstract: A prediction control apparatus for a robot and a machine tool in which a future target value has already been known and which enables a robot and a machine tool to follow up a target command at a high speed and with a high accuracy without requiring a learning step. This apparatus prediction determines a control input which is outputted at every predetermined sampling cycle to an object to be controlled so that an output from the object to be controlled agree with a target command in such a manner that a future deviation predictor determined on the basis of a model of the object to be controlled becomes minimal. The apparatus consists of: (a) means for inputting outputs of a future target command and an object to be controlled, (b) means for storing a future target command, a prediction control constant and a past control input, (c) means for determining a follow-up deviation on the basis of the difference between a target command and an output from the object to be controlled, and (d) means for computing a control input, which is outputted to the object to be controlled, on the basis of a future target command, an output of the object to be controlled, a past control input, a deviation and a model of the object to be controlled, in such a manner that an evaluation function including weighted square sum of a predicted value of a future deviation becomes minimal.

Description: TECHNOLOGICAL FIELD The present invention relates to a predictive control apparatus for, among other things, robots and industrial machinery, where a future target value is already known, according to the preamble of claim 1. BACKGROUND ART In conventional design methods with repeat target values, using the control deviation occurring one cycle previously along with the control deviation occurring at the cycle previous to that cycle at a time t' with respect to the current time t, the deviation for the time from the occurrence of the one previous cycle and the deviation from the time t', to decide the control input for this time, although particularly useful, is not widely used. In order to solve this kind of problem, in U.S. Patent No. 5,119,287, or EP-A-0 333 870, the increment was corrected for each sample taken, and then by using the deviation for M samples taken from the previous sample, the current deviation, the N-1 past corrected increment, the operation amount for the previous sample, and constants decided beforehand, an algorithm was obtained to decide only by using arithmetic the amount of control for a periodic target value which is most suitable for the tracking. In another method, with a control object having a target value with a pattern which repeats itself periodically, the control deviation for the period during the current and past cycles, the past increment correction, information concerning the motion characteristics of the control object (the indicial response) and the control input or information classified with these, are used with each of the sampling times for every control input to select the squared weighting of the future control deviation predictive, to be minimam. By these methods, a control algorithm for the tracking is obtained where the control amount is that most suited to the tracking. This can be realized by a mainly digital circuit or a microcomputer so that it becomes possible to have a control system which can produce much more highly accurate tracking. However, the learning control method disclosed in U.S. Patent No. 5,119,287 requires a learning process. Also, a large number of data memories are needed as it is necessary to have deviations for past cycles. Prior art control methods which improve speed, precision and stability of control systems by utilizing future output values predicted from future target values or modeling of control objects, are known as follows: In the control method of EP-A-0 037 579, a dynamic output vector of a process is predicted by using an adaptive-predictive model, and a control vector to be applied to the process is computed with the objective so that the predicted dynamic output vector becomes equal to a desired dynamic output vector and with a further objective so that the desired dynamic output vector and the control vector optimize a chosen performance criterion in which a future reference trajectory is explicitly considered. The reference trajectory is periodically redefined as a function of the previously measured process output and evolving according to desired dynamics to the set point. The adaptive-predictive model can be updated at each sampling instant from the comparison of an estimated value of either an output vector, a control vector or a disturbance vector of the system being controlled, which vector is obtained with the use of the adaptive-predictive model, and the actual value of the corresponding output, control or disturbance vector. However, the method according to EP-A-0 037 579 has the problems that a reference trajectory corrected periodically is necessary to be provided newly and that computations become large and complex because the adaptive-predictive model is updated at each sampling instant. In the control method of JP-A-62 118 405, a control algorithm which may optimize a response to a future target value can be obtained by describing a control object with a step response, and by determining an incremental manipulated variable of the time of respective samplings by only the four fundamental rules of arithmetic by using N future target values, a present control variable, N-1 past manipulated variables and predetermined constants. The method according to JP-A-62 118 405, however, needs past manipulated variables of sampling numbers until the step response becomes stable, and it has the problem that necessary memories and computing period increase. The control system known from JP-A-61 256 405 relates to an adaptive control apparatus with STR (Self Turning Regulator), and control stability is improved by the system because it can model a control object with ARMA (Auto Regressive Average) on the basis of observing data. In this method, however, a response to a future target value is not improved any more because the future target value is not used. The control system disclosed in JP-A-63 10 201 is a control apparatus which controls continuous time control object, and it improves a tracking performance to a target value by including two binary switching operating means which change a control variable to different directions, detecting the control variable at each given sampling instant, executing an operation by either of binary switches for the control object at each sampling instant and simultaneously estimating parameters of the model of the control object on the basis of past control variables and manipulated variables, estimating a control sequence predictable as its response when manipulated variables extended over several future steps are applied to the model, using first and second on-off adaptive control means of dispersing time type which control the continuous time control object so as to become an optimal control variable, and controlling the continuous time control object by using either of the binary switching operating means with selectively switching either of the on-off adaptive control means by the target value and the present control variables and manipulating variables. This system, however, has the following problems: Calculations are frequent and complex because estimating and updating should be performed at each sampling instant in order to adapt the model to the control object; and the on-off adaptive control means are used which are not effective for a control object with a small time constant. It is therefore the basic object of tnis invention to propose a predictive control apparatus which provides highly accurate tracking at high speeds according to a target instruction without a learning process being required. Further, more specifically, this invention has the following objects. (i) To reduce the number of data samples. (ii) To obtain a control input without using a control object output. (iii) To reduce the amount of memory required and shorten the amount of calculation time. (iv) To carry out tracking with the most suitable target instruction, even in control systems where the output increment is detected K samples late. (v) To provide a predictive control apparatus with the target instruction most suitable for tracking without vibrations occurring in the response. (vi) To provide a predictive control apparatus with the target instruction most suitable for tracking, even in the case where a control input increment decided at a current time i is used one sample later at a time i+1. (vii) To provide a predictive control apparatus with the target instruction most suitable for tracking, which does not vibrate even for very high frequency instructions or when there is external disorder. (viii) To provide an incremental type predictive control apparatus with the target instruction most suitable for tracking, which does not vibirate even for very high frequency instructions or when there is disturbance and which achieves this while requiring less memory and a smaller amount of calculation time. (ix) To provide a predictive control apparatus which can be applied to a control object which has two constituent integral portions. (x) To provide a predictive control apparatus in which detrimental effects due to model errors are minimam. (xi) To provide a predictive control apparatus corresponding to a late-detected control target output where the amount of calculations carried out during each sampling period is small. (xii) To provide a predictive control apparatus which corresponds to changes in the characteristics of a control object, even if before that point in time the operator of the equipment did not know the control object model or only set the target model once. (xiii) To provide an incremental type predictive control apparatus as in the eighth object, but which involves few integration and summing calculations, less memory and a shorter calculation time. (xiv) To provide a predictive control apparatus as in the tenth object, but which requires little memory and less calculation time. (xv) To provide a predictive control apparatus as in the seventh object, but which can be applied to the case where speed control can be carried out by deciding an acceleration or torque instruction from a target speed instruction. (xvi) To provide apredictive control apparatus as in the sixth object, but where little memory and less calculation time is required. DISCLOSURE OF INVENTION The objects of the present invention are solved by a predictive control apparatus of claim 1. The claims 2 to 41 describe preferred embodiments of the invention. In order to solve the problems encountered in the prior art, the present invention especially provides a predictive control apparatus as set forth in claim 1. BRIEF DESCRIPTION OF DRAWINGS FIG. 1 is a block diagram of the structure of a first embodiment of the present invention; FIG. 2 is a graph of an example of the step response for a control object; FIG. 3 is a graph of an example of the weighting W m for this embodiment; FIG. 4 a graph of a further example of the weighting W m for this embodiment; FIG. 5 is a graph describing the operation of this embodiment; FIG. 6 is a further graph describing the operation of this embodiment; FIG. 7 is another graph describing the operation of this embodiment; FIG. 8 is a block diagram of the structure of a second embodiment of the present invention; FIG. 9 is a graph describing the operation of this embodiment; FIG. 10 is a block diagram of the structure of a third embodiment of the present invention; FIG. 11 is a graph describing the operation of this embodiment; FIG. 12 is a further graph describing the operation of this embodiment; FIG. 13 is a block diagram of the structure of a fourth embodiment of the present invention; FIG. 14 is a graph describing the operation of a fifth embodiment of this invention; FIG. 15 is a graph describing the operation of this embodiment; FIG. 16 is a block diagram of the structure of a sixth embodiment of the present invention; FIG. 17 is a graph describing the operation of this embodiment; FIG. 18 is a further graph describing the operation of this embodiment; FIG. 19 is a block diagram for the case where a seventh embodiment of this invention is applied to a position control system; FIG. 20 is a further block diagram of this embodiment; FIG. 21 is a graph of an example of the step response increment for the control object; FIG. 22 is a graph describing the operation of this embodiment; FIG. 23 is a block diagram for the case where an eighth embodiment of this invention is applied to a position control system; FIG. 24 is an internal block diagram of this embodiment; FIG. 25 is a description of the operation of this embodiment; FIG. 26 is a block diagram of a ninth embodiment of this invention; FIG. 27 is a graph of the characteristics of an incremental series for this embodiment; FIG. 28 is a further graph of the characteristics of an incremental series for this embodiment; FIG. 29 is a block diagram of the structure of the control object; FIG. 30 is a block diagram of a tenth embodiment of this invention; FIG. 31 is a block diagram of an eleventh embodiment of this invention; FIG. 32 is a block diagram of a twelfth embodiment of this invention; FIG. 33 is a block diagram of a further example of a twelfth embodiment of this invention; FIG. 34 is a block diagram of a thirteenth embodiment of this invention; FIG. 35 is a block diagram of a fourteenth embodiment of this invention; FIG. 36 is a block diagram of a fifteenth embodiment of this invention; and FIG. 37 is a block diagram of a sixteenth embodiment of this invention. BEST MODE FOR CARRYING OUT THE INVENTION The following is a detailed description of the present invention based on the embodiments. FIG. 1 is a block diagram of the structure of an embodiment to attain the first object of said present invention. In this diagram, the numeral 1 indicates an instruction generator, the numeral 2 indicates the memory for the future target instruction r(j) (j=i+1, i+2, ..., i+M), the numeral 3 indicates the memory for the constants q 1 , q 2 , ..., q M , Q, g 1 , g 2 , ..., g N-1 , and the numeral 4 indicates the memory for the past control input u(j) (j=i-1, i-2, ..., iN+1). Also, the numeral 5 indicates the arithmetic unit where the control input u(i) for this time is calculated according to the equation: The numerals 6 and 7 indicate samplers which commence sampling upon a sampling period T, and the numeral 8 indicates a hold circuit. The numeral 9 indicates the control object, with an input u(t) and an amount subjected to control at the output x(t). Blocks 2 to 8 represent the control system which is the section usually known as the "controller" section and can be easily realized by a mainly digital circuit or microcomputer. Any kind of preferred control system can be included as the control system within the control object 9 (i.e. a compensator). Herein, deductions are made from equation (1-1). Sampling is carried out only for the first N step responses for the control object 9. After this, if it is approximated that the increasing portion h K is reduced by the damping ratio P, the pulse propagation function can be shown to be; G(z)={b 1 z -1 +b 2 z -2 + ... b N z -N }/(1-Pz -1 ) b k =h k -Ph k-1 Where h j (j = 1, 2, ..., N), provided that the unit step response sample H j is increasing, as is shown in FIG. 2, it can be shown that h j =H j -H j-1 . ("Digital System Control" p224-p225 published by Shokodo, written by Seinosuke Narita) It follows that the output x(i) with respect to time i can be written as; Herein, if the control input u(j) (j= i+1, i+2,...) at time i+1 is assumed to be the same as u(i), an output estimate x*(i+m) for time i+m can be considered from the following equation; Accordingly, the estimation of future deviation e*(i+m) becomes; Now, the weighted square sum J of the future deviation estimates is taken as the evaluation function, and the control input u(i) is chosen so that J is at a minimum. Herein, W m is the weighting coefficient for the future deviation estimate e*(i+m) and this example is shown in FIG. 3 and FIG. 4. The control input u(i) for making the evaluation function J a minimum can be derived from equation (1-1) as being ; ∂J/∂u(i)=0 provided that; b k =h k -Ph k-1 Also, as is shown in FIG. 2, q m , Q and g n measure the control object step response, and by assigning a suitable weighting function W m , this can be measured beforehand. FIG. 5 is a view of an example of the operation for the case where this invention is used with a position control system for a D.C. servo motor and FIG. 6 is a view of an example of the operation of a conventional position control system. In FIG. 5 and FIG. 6, the character r represents the object position instruction for the motor, x indicates the response and e indicates the deviation. FIG. 7 shows the step response and, whereas with the conventional method (for example, Japanese Unexamined Patent Publication No. 62-118405) it was necessary for N to be 100, with the present invention N need only be 5. In the method described above, according to this embodiment, using the future target instruction and control object output, the past control input and previously set constants, according to simple four-rule arithmetic, an estimation control system with a suitable response for the target instruction can be realized and the sampling period can be greatly reduced. Next, a description of an embodiment of a predictive control apparatus for attaining a second object of the aforementioned present invention will be given. FIG. 8 is a block diagram of the structure of this embodiment. In the same diagram, the numeral 11 represents the instruction generator, numeral 12 the memory for the future target instruction r(j) (j=i+1, i+2, ..., i+M), numeral 13 represents the memory for the constants q 1 , q 2 , ..., q M, g 1 , g 2 , ..., g N-1 and the numeral 14 represents the memory for the past control input u (j=i-1, i-2, ..., i-N+1). Also, 15 is the arithmetic unit, where the control input u(i) is calculated according to the equation: The numeral 16 indicates a sampler which commences sampling upon a sampling period T, and the numeral 17 indicates a hold circuit. The numeral 18 indicates the control object, with an input u(t) and an amount subjected to control at the output x(t). Herein, deductions will be made from equation (2-1). It can be shown that the output x(i) for the control object 18 is: x(i) = h 1 u (i-1) + h 2 u (i-2) + ... + h N u(i-N) Where h j (j=1, 2, ..., N), provided that the unit step response sample H j is increasing, as is shown in FIG. 2, it can be shown that h j =H j -H j-1 . Also, N should be chosen so that the response settles sufficiently. Herein, if the control input u(j) (j=i+1, i+2, ...) at time i+1 is assumed to be the same as u(i), an output estimate x*(i+m) for time i+m can be considered from the following equation; Accordingly, the future deviation estimate e*(i+m) becomes: Now, the weighted square sum J of the future deviation estimates e*(i+m) is taken as the evaluation function, and the control input u(i) is chosen so that J is at a minimum. Herein, W m is the weighting coefficient for the future deviation estimate e*(i+m) and this example is shown in FIG. 3 and FIG. 4. The control input u(i) for making the evaluation function J a minimum can be derived from equation (2-1) as being ; ∂J/∂u(i)=0 provided that; Also, as is shown in FIG. 2, q m , Q and g n measure the control object step response, and by assigning a suitable weighting function W m , this can be measured beforehand. FIG. 9 is a view of the experimental results for the case where the present invention is used in the position control system for a D.C. servo motor. Here, the character r indicates the target position instruction for the motor, x indicates the response and e indicates the deviation. In the method described above, according to this embodiment, using the future target instruction, the control object output and previously set constants, according to simple four rule arithmetic, an estimation control system with a suitable response for the target instruction can be realized. Moreover, the control input can be calculated beforehand so that the control object output is not used for the control input request. Next, an explanation will be given of an embodiment to attain a third object of the present invention. FIG. 10 is a block diagram of the structure of this embodiment and in the same diagram, the numeral 21 indicates the instruction generator, numeral 22 indicates the memory for the future target instruction increment Δr(j) (j=i+1, i+2, ..., i+M), numeral 23 indicates the memory for the constants Q, E, v 1 , v 2 , ... v M , g 1 , g 2 , ..., g N-1 and numeral 24 indicates the memory for the past control input increment Δu(j) (j=i-1, i-2, ..., i-N+1). Also, the numeral 25 indicates the arithmetic unit, where the control input increment Au(i) is calculated according to the equation: The numeral 26 indicates the motor and this position control apparatus, which takes a target instruction increment, inputs the control increment Δu(i), and outputs the actual position of the motor y(i). The numeral 27 indicates the differentiator which obtains the motor position increment Δy(i), numeral 28 indicates the subtracter which obtains the deviation increment Δe(i) and numeral 29 indicates the integrator which obtains the deviation e(i) from Δe(i). Herein, deduction are made from equation (3-1). The future deviation estimate is estimated from the the next equation. Here, h k (k=1, 2, ...) is the increment (h K =H K -H K-1 ) for the samples H k (k=1, 2, ...) taken over control object unit step response sample intervals T. Also, P is the damping ratio after N samples and for example, if the integer coincides with the gain constant K s it is preferable to deduce that P=1-{h N /(K s -H N-1 )}. Here, N is the number of samples which the step response can be raised up to. According to equation (3-2), the estimate of the future deviation increment Δe*(i+m) is: so that an estimate can be made. Now, the future deviation increment estimate Δe*(i+m) and the weighted squared sum J for the deviation e(i): are taken as the evaluation function and the control input increment Δu(i) is chosen so that J is at a minimum. Here, W m is the weighting for the future deviation, an example of which is shown in FIG. 3. It follows that Δu(i) for making the evaluation function J minimum according to ∂J/∂Δu(i)=0 can be derived from the aforementioned equation (3-1) if it is assumed that the future control input increment Δu(i+1), Δu(i+2), ..., Δu(i+M)=0. Also, as is shown in FIG. 2, v m , Q, g n and E measure the control object step response, and by assigning a suitable weighting function W m , this can be measured beforehand from the following equation: T k =1+PT k-1 b k =h k -Ph k-1 P=1-{h N /(K s -H N-1 )} FIG. 11 is a view of an example of this embodiment applied to the operation of a D.C. servo motor position control system and FIG. 12 is a view of an example of the operation of a conventional position control system. In FIG. 11 and FIG. 12, r is the target position instruction for the motor, y is the response and e is the deviation. Also, for the case where the instruction is input as the control object rather than the instruction increment, the integral u(i) of the output Δu(i) from the arithmetic unit 5 is obtained and it is preferable to then output the control object. Here, an explanation has been given for the case of this embodiment being applied to a first embodiment apparatus. However, this embodiment can be applied to methods other than this where the future increment is estimated and the current control input increment is decided so that the evaluation function equation (3-4) becomes a minimum. As mentioned above, according to the present embodiment, operation can be achieved by using less memory in less time. Next, an explanation will be given of an embodiment to attain a fourth object of said present invention. Here, data is taken in, a predetermined calculation is carried out and the result is output, all in the space of one sampling cycle. For example, [the output increment detection is two samples late] means that the output increment taken in this period is that from the time before the previous time. FIG. 13 is a block view of the structure of this embodiment. In the same diagram, the numeral 31 indicates the instruction generator, numeral 32 indicates the memory for the target instruction increment Δr(j) (j=i-K+1, i-K+2, ..., i+M), numeral 33 indicates the memory for the constants Q, E, V -K+1 , V -K+2 , ..., V M , g 1 , g 2 , ..., g N+K-1 and the numeral 34 indicates the memory for the past control input increments Δu(j) (j=i-1, i-2, ..., i-N-K+1). Also, FIG. 35 is the arithmetic unit where the control input increment is calculated according to the equation: The numeral 36 indicates the motor and its position control system, which takes a target instruction increment, inputs the control increment Δu(i), and outputs the actual position of the motor y(i-K) which is K sampling time late. The numeral 37 indicates the differentiator which obtains this increment Δy(i-K) and numeral 38 indicates the memory for storing the target instruction increment occurring at time i-K. Numeral 39 indicates the subtracter which obtains the deviation increment Δe(i-K) and numeral 40 indicates the integrator. Herein, deductions will be made from equation (4-1). The future deviation increment estimate Δe*(i+m) can be estimated from: where h N+j =P j h N (j=1, 2, ...), and h j is the increment (h j =H j -H j-1 ) for samples H j (j=1, 2, ...) taken over the kind of control object unit step response sample periods T shown in FIG. 2. Also, P is the damping ratio for the increment h n after N samples and for example, if the integer coincides with the normal fixed gain constant K s it is preferable to deduce that P=1-{h N /(K s -H N-1 )}. Here, N is the number of samples which the step response can be raised up to. Now, the future deviation increment estimate Δe*(i+m) and the squared weighting J for the deviation e(i-K) where are taken as the evaluation function and the control input increment, Δu(i) is chosen so that J is at a minimum. Here, W m is the weighting for the future deviation, an example of which is shown in FIG. 3. It follows that Δu(i) for making the evaluation function J minimum according to ∂J/∂u(i) can be derived from the aforementioned equation (4-1) if it is assumed that the future control input increment Δu(i+1), Δu(i+2), ..., Δu(i+M)=0. Also, v m , Q, g n and E measure the control object step response shown in FIG. 2 and by assigning a suitable weighting function W m , this can be calculated beforehand from the next equation: (h n =0 (n≤0), h N+j =P j h N (j=1,2, .. N+m+1)) P=1-{h N /(K s -H N-1 )} As described above, according to the present embodiment, even in control systems where the detection of the output increment is K sampling time late, a predictive control algorithm to give the most suitable target instruction for tracking can be acquired. Next, an explanation will be given of an embodiment to attain a fourth object of said present invention. It is preferable if this embodiment is considered to be the same as the embodiment to attain the fourth embodiment of the invention but with the evaluation function in the equation (4-3) being made to be: it being preferable to make each constant as follows; q m =W m H m /(A+C) P=1-{h H /(K s -H N-1 )} (h n =0 (n≤0), h N+j =P j h N (j=1,2, .. N+m+1)) FIG. 14 is a view of an example of the operation for the case where this embodiment is applied to the position control system of a D.C. servo motor, and FIG. 15 is a comparative view of an example of the operation for the case where the apparatus for the aforementioned third embodiment are used. In FIG. 14 and FIG. 15, r is the target position instruction for the motor, y is the response and e is the deviation where the settings are N=10, M=5 and C=0.005. As described above, according to the present embodiment, apredictive control algorithm can be acquired where the target instruction is the most suitable for the tracking and where the response does not vibrate. Next, an embodiment to attain a sixth object of the present invention will be described. This embodiment can be applied to the case where the detection of the output increment is K samples late but, here, the case where the detection of the output increment is not late will be described. FIG. 16 is a block diagram showing the structure of this embodiment. In the same diagram, the numeral 51 indicates the instruction generator, numeral 52 indicates the memory for the target instruction increment Δr(i+M), Δr(i+M-1), ..., Δr(i), numeral 53 indicates the memory for the constants Q, E, V M , V M-1 , ..., V 1 , g 0 , g 1 , ..., g N-1 , and numeral 54 is the memory for the past control input increment Δu(i) Δu(i-1), ..., Δu(i-N+1). Also, numeral 55 indicates the arithmetic unit where the control input increment Δu(i+1) occurring at the present time i is calculated according to the equation: The numeral 56 indicates the control object, where the control input increment Δu(i+1) taken to be the target instruction increment is input, and y(i) is output. Numeral 57 indicates the differentiator which obtains the increment Δy(i), 58 is the subtracter which obtains the deviation increment Δe(i) and 59 is the integrator. Herein, deductions are made from equation (6-1). The future deviation increment estimate Δe*(i+m) can be estimated from: where h j is the increment (h j =H j -H j-1 ) for samples H j (j=1, 2, ...N) taken over the kind of control object unit step response sample periods T shown in FIG. 2, and it is assumed that this is reduced (h N+j =P J h N (j=1,2, ...)) by the damping ratio P after N samples. Here, for example, if the integer coincides with the normal fixed gain constant K s it is preferable to deduce that P=1-{h N /(K s -H N-1 )}. Now, the control input increment Δu(i+1) occurring at the current time i is chosen an evaluation function which is related to the estimate Δe*(i+m) of the future deviation increment, and where the deviation e(i) and the control input increment Δu(i+1) are at a minimum. Here, W m is the weighting which occurs at the future time i+m, an example of which is shown in FIG. 3. Also, C is the control input increment weighting, and this weighting should be the correct amount. It follows that Δu(i+1) for making the evaluation function J a minimum according to ∂J/∂Δu(i+1)=0 can be derived from the aforementioned equation (6-1) if it is assumed that the future control input increment Δu(i+2), Δu(i+3), ..., Δu(i+M)=0. Also, v m , Q, g n and E measure the control object step response shown in FIG. 2 and by assigning a suitable weighting function W m , this can be calculated beforehand from the next equation: q m =W m H m-1 /(A+C) P=1-{h N /(K s -H N-1 )} (h n =0 (n≤0), h N+j =P j h N (j=1,2, .. N+m+1)) FIG. 17 is an example of the operation for the case where this embodiment is applied to a single axis slider position control system where a D.C. servo motor acts as the control object, and FIG. 18 is an example of the operation of the apparatus for the fourth embodiment shown for comparison. In FIG. 17 and FIG. 18, r is the target position instruction, y is the response, e is the deviation and only the deviation is expanded. Here, the sampling period was 1 ms and the settings were N=5, M=10, W m =h m and C=0.02. As described above, according to this embodiment, a predictive control algorithm can be acquired to give the target instruction most suited for the tracking even for the case where a time of i+1 for 1 sample following is used because the control increment decided at the current time i is late due to calculations and data transmissions. Next, an embodiment to attain a seventh object of the present invention will be described. FIG. 19 is a block diagram for the case where this embodiment is applied to a position control system for a motor. In this diagram the numeral 61 indicates the predictive control apparatus, where the future target instruction r(j) (j=i, i+1, i+2, ..., i+M) and the motor position y(i) are inputted, and the speed instruction v(i) is outputted to the speed controller 62. Numeral 63 indicates the motor and numeral 64 indicates the motor position detector. Next, a block diagram of the internal workings of the predictive control apparatus is shown in FIG. 20. In this diagram, the numeral 71 indicates the memory for the future target instruction r(j), numeral 72 indicates the memory for the constants q 1 , q 2 , ..., q M , Q, E, g 1 , g 2 , ..., g N-1 and numeral 73 indicates the memory for the past speed instruction v(j) (j=i-1, i-2, ..., i-N+1). Also, numeral 74 indicates the arithmetic unit where the speed instruction v(i) is calculated according to the equation: Here, deductions are made from equation (7-1). It can be shown that the output y(i) from the motor 63 is y(i)=H 1 v(i-1)+H 2 v(i-2)+ ... +H N v(i-N)+ ... where H j (j=1, 2, ..., N) is the output increment when the unit step instruction is input into the speed controller 62. Also, N should be chosen so that the response settles sufficiently. Similarly, the future output predictive y*(i+m) can be given from the following equation. y*(i+m)=H 1 v(i+m-1)+H 2 v(i+m-2)+H 3 v(i+m-3)+ ... +H N v(i+m-N)+... Here, if it is assumed hereinafter that after time i+1, v(j) (j=i+1, i+2, ...) is zero, the future deviation predictive e*(i+m) becomes Now, is taken to be the evaluation function and the speed instruction v(i) is chosen so that J becomes a minimum. Here, W m is the weighting for the future deviation predictive e*(i+m), an example of which is shown in FIG. 3 and FIG. 4. Also, A and C are real numbers above zero. As ∂J/∂v(i)=0 v(i) can be given from equation (7-1) to make J a minimum. E=QA Also, q m , Q, E, and g n measure the step response increment in FIG. 21, and by assigning the weighting W m , A and C accordingly, calculations can be carried out beforehand. FIG. 22 is a view of an example of the operation for the case where this embodiment is used in a single axis slider position control system for a D.C. servo motor. In FIG. 22, r indicates the target instruction for the motor, y indicates the response and e indicates the deviation. In the above description, according to this embodiment, by using a future target instruction, a past speed instruction, a control target output and constants which were determined beforehand, and by making a speed instruction using simple four rule arithmetic, a predictive control apparatus can be realized which gives a high frequency instruction, which responds to external disorder and which is free from vibrations. Next, an embodiment to attain an eighth object of the present invention will be described. FIG. 23 is a block diagram of the case where this embodiment is applied to a position control system for a motor. In this diagram, the numeral 81 indicates incremental predictive control apparatus, where the future target instruction increment Δr(j) (j=i, i+1, i+2, ..., i+M) and the motor position increment Δy(i) are inputted, and the speed instruction v(i) is outputted to the speed controller 82. Numeral 83 indicates the motor, numeral 84 indicates the motor position detector and 85 is the differentiator. Next, a block diagram of the internal workings of the incremental predictive control apparatus 81 is shown in FIG. 24. In this diagram, the numeral 91 indicates the memory for the future target instruction increment Δr(j), numeral 92 indicates the memory for the constants v 1 , v 2 , ..., v m , Q, E, F, g 1 , g 2 , ..., g N-1 . Numeral 93 indicates the memory for the past speed instruction increment Δv(j) (j=i-1, i-2, ..., iN+1) and numeral 94 indicates the memory for the past sampling speed instruction v(i-1). Also, numeral 95 indicates the arithmetic unit where the speed instruction increment Δv(i) is calculated according to the equation Here, derivations are made from equation (8-1). The output increment Δy(i) for the motor 83 can be shown to be Δy(i)=H 1 Δv(i-1)+H 2 Δv(i-2)+ ... +H N Δv(i-N)+... where H j (j=1, 2, ..., N) is the output increment when the unit step instruction is input into the speed controller 82. Also, N should be chosen so that the response settles sufficiently. Similarly, the future output preview Δy*(i+m) can be given from the following equation. Δy*(i+m)=H 1 Δv(i+m-1)+H 2 Δv(i+m-2)+ ... +H N Δv(i+m-N)+... Here, if it is assumed hereinafter that after time i+1, Δv(j) (j=i+1, i+2, ...) is zero, the future deviatior predictive Δe*(i+m) becomes Now, is taken to be the evaluation function and the speed instruction v(i) is chosen so that J becomes a minimum. Here, W m is the weighting for the future deviation predictive an example of which is shown in FIG. 3 and FIG. 4. Also, A is at least one and C is a real number above zero. As ∂J/∂Δ(i)=0 v(i) can be given from equation (8-1) to make J a minimum, q m =W m L m /B F=C/B Also, v m , Q, E, F and g n measure the step response increment in FIG. 21 and by assigning the weighting W m , A and C accordingly, calculations can be carried out beforehand. The numeral 96 indicates the arithmetic unit where a speed instruction increment is calculated and a speed instruction v(i) is made. FIG. 25 is a view of an example of the operation for the case where this embodiment is used in a single axis slider position control system for a D.C. servo motor. In FIG. 25, r indicates the target instruction for the motor, y indicates the response and e indicates the deviation. As described above, according to this embodiment, an incremental predictive control apparatus is provided to give high frequency instructions which respond to external disorder without the occurrence of vibrations, and to provide the target instruction most suited to tracking while requiring less memory and calculation time. Next, an embodiment to attain a ninth object of the present invention will be described with reference to FIG. 26. In this diagram, the numeral 101 indicates the predictive control apparatus for this embodiment, where the future target instruction r(i+M) and the control object output y(i) are inputted at the current time i, and the control input u(i) is outputted to the control object. The numeral 106 indicates a differentiator which obtains increments during sampling periods, numeral 102 indicates the memory for future target instruction increment Δr(i), Δr(i+1), ..., Δr(i+M), numeral 108 indicates the subtracter which obtains the difference between the target instruction increment Δr(i) stored in the memory 102 and the output increment Δy(i) and numeral 107 indicates the integrator which obtains the deviation e(i) from Δe(i). The numeral 103 indicates the memory for the constants Q, E, v 1 , v 2 , ..., V M , g 1 , g 2 , ..., g N-1 and numeral 104 indicates the memory for the past control input. The numeral 105 indicates the arithmetic unit where the control input u(i) is calculated according to the equation: The calculated output control u(i) is outputted from the predictive control apparatus 101 so that it can be outputted to the control target and it is also stored in the memory 104. Here, deductions are made from equation (9-1). The increment series {I 1 , I 2 , ...} during the sampling period T s for this unit step response sample increases linearly after N samples so that the control object has two constituent integral portions (FIG. 27). It follows that the increment series {H n (H n =I n -I n-1 )} for the series {I 1 , I 2 , ...} will become {H 1 , ..., H N , H N , ...} (FIG. 28). Now, taking the following as the model during the control object input u(i) and the output y(i): y(i)=I 1 u(i-1)+I 2 u(i-2)+ ... it can be shown that the increment Δy(i) for y(i) is: and furthermore, Δy(i+m) can be shown to be: Here, taking the future time control input u(i+m)=0, (m=1, 2, ..., M), the predictive Δy*(i+m) for the future output increment can be given by the following equation according to equations (9-3) and (9-4). It follows that the future deviation predictive e*(i+m) is given by the next equation: The control input u(i) is set so that the evaluation function becomes a minimum, and if ∂J/∂u(i)=0, the aforementioned equation (9-1) can be obtained. Each constant v m , Q, E and g n can be given from the next equations. An actual measured value or a value simulated from an item, such as the transmission function for the control object, can be used with the increment series for the control object unit step response sample. Particularly, in the case of the system shown in FIG. 29 where the control object is made up of a sampler which samples for a sampling period T s , a 0th hold, has a gain K and has two integrators: I 1 =KT s 2 /2 I n =KT s 2 /2+(n-1)KT s 2 (n≥2) H 1 =KT s 2 /2 H n =KT s 2 (n≥2) so, each of the constants v m , Q, E and g n are calculated according to the sampling period T s and the gain K. In particular, if g becomes: g n =0 (n≥2) then equation (9-1) will become: Further, when the control object which inputs the torque instruction and outputs the position is a motor and a controller, the gain K can be calculated according to the gain of the torque controller and the inertia of the motor and load. In the way described above, according to this embodiment, a predictive control apparatus to which a control object having two constituent integral portions can be applied may be realized. Next, an embodiment to attain the tenth object of this invention will be described with reference to FIG. 30. In this diagram, the numeral 111 indicates the predictive control apparatus for this embodiment which inputs at the current time i a future target instruction r(i+M), and a control object output y(i-K) sampled K (where K is greater than or equal to zero) samples previously, and outputs a control input u(i) to the control object. Numeral 112 indicates the memory for storing the target instruction r(i-K), r(i-K+1), ..., r(i+M), and numeral 117 indicates the subtracter which obtains the difference e(i-K) between the target instruction r(i-K) stored in the memory 112 and the output y(i-K). The numeral 113 indicates the memory for storing the constants Q 0 , Q 1 , E, q 1 , ..., q m , g 1 , ..., g N+K , numeral 114 indicates the memory for storing the output y(i-K-1) and numeral 115 indicates the memory for storing the past control input. The numeral 116 indicates the arithmetic unit where the control input u(i) is calculated according to the equation: The calculated output control u(i) is outputted from the predictive control apparatus 111 so that it can be outputted to the control target, and it is also stored in the memory 115. Here, deductions are made from equation (10-1). The first N samples {H 1, ... H N } for the unit step response of the control object are taken, and if the damping of this increment h n =H n -H n-1 (n>N) is assumed hereinafter to have a constant ration P, a control object model can be acquired from the next equation. where b n =h n -Ph n-1 . The future output at a time i can then be estimated from the next equation. y*(i+m)= y (i+m)+y(i-K)- y (i-K) According to equation (10-3), this becomes where h N+j =P j h N (j>0). Further, if y is used in place of y and, this becomes: where h N+j =P j h N (j>0). It follows that the future deviation estimate e*(i+m) is given by the following equation: e*(i+m)=r(i+m)-y*(i+m) and if the control input u(i) is selected so that the evaluation function becomes a minimum, by putting ∂J/∂u(i)=0 equation (10-1) can be obtained. Each of the constants q m , Q 0 , Q 1 , E and g n can then be given by the next equations. where h j =0 (j≤0) and h N+j =P j h N (j≥1). An actual measured value or a value simulated from an item, such as the transmission function for the control object, can be used with the series for the control object unit step response sample. In the method described above, according to the present invention, a predictive control apparatus with a highly accurate tracking operation can be obtained in which detrimental effects due to model errors are few. Next, an embodiment to attain the eleventh object of this invention will be described with reference to FIG. 31. In this diagram, the numeral 121 indicates the predictive control apparatus for this embodiment which inputs at the current time i a future target instruction r(i+M), and a control object output y(i-K) which was sampled K (K≥0) samples previously, and outputs a control input u(i) to the control object. Numeral 122 indicates the memory for storing the target instruction r(i-K), r(i-K+1), ..., r(i+M), and numeral 127 indicates the subtracter which obtains the difference e(i-K) between the target instruction r(i-K) stored in the memory 122 and the output y(i-K). The numeral 123 indicates the memory for storing the constants E, q 1 , ..., q M , p 0 , ..., P Na , g 1 , ..., g Nb+K , numeral 124 indicates the memory for storing the output y(i-K), ..., y(i-K-Na) and numeral 125 indicates the memory for storing the past control input u(i), ..., u(i-K-Nb). The numeral 126 indicates the arithmetic unit where the control input u(i) is calculated according to the equation: The calculated output control u(i) is outputted from the predictive control apparatus 121 so that it can be outputted to the control target, and it is also stored in the memory 125. Here, deductions are made from equation (11-1). Now, assuming that the transfer function model for the control object is acquired from the dispersion time system for the model between the input and the output becomes that of the next equation. At a time i, by using an actual measured value for the model output y(i+m)(where m is greater than or equal to -K+1) from that point on so that an actual measured value up until a time i-K for the output y(i-n) (where n is greater than or equal to K) can be acquired, it can be shown that and accordingly becomes the following equation: Here, if the future control input u(j)=0 (j>i) , the coefficients â mn and b and mn are given by: â (-K+1)n =a (n-K+1) m=-K+1, K≤n≤N a +K-1 b (-K+1)n =b (n-K+1) m=-K+1, 0≤n≤N b +K-1 where a n =0 (n>N a ) and b n =0 (n< 1 and n>N b ). Also, if u(j)=u(i) (j>i), b and m0 in equation (11-6b) will become: Here, if the output after the time i-K is measured by m≥-K+1 the output estimate y*(i+m) can be shown to be: and the coefficients A mn and B mn are given by the following equations. where b n =0(n<1), â m(Na+K) =b and m(Nb+K) =0. Also, y and on the right hand side of the equation (11-3b) is substituted for y* in equation (11-7) and substitute y and(i+m) from (11-3a) and (11-3b') into (11-7), so that y*(i+m) can then be obtained recursively. In this case, if u(j)=0 (j>i), then the coefficients A mn and B mn in equation (11-8) can be given by A (-K+1) =1+a 1 m=-K+1, n=K A (-K+1)n =a (n-K+1) -a (n-K) m=-K+1,K<n≤Na+K B mn =b (n+m)-b(n-K) m=-K+1, 0≤n≤N b +K where a n =0 (n>N a ), b n =0 (n<1 or n>N b ) and if u(j)=u(i) (j>i), B m0 in equation (11-11b) can be expressed by the next equation. According to the above, the output estimate y*(i+m) (m≥K+1) can be obtained from equation (11-8) and the A mn and B mn can be obtained from equations (11-5), (11-6), (11-9), (11-10) and (11-11). It follows that the future deviation estimate e*(i+m) can be obtained from the equation e*(i+m)=r(i+m)-y*(i+m) 1≤m≤M and by setting the control input u(i) to make the evaluation function a minimum equation (11-1) can be obtained by making ∂J/∂u(i)=0. Each of the constants q m , p n , E, and g n can then be obtained from the following equations. Also, for the case where is taken as the evaluation function in place of the evaluation function in equation (11-12), g 1 in equation (11-13) becomes as follows: As described above, according to this embodiment, a predictive control apparatus with a more accurate tracking operation can be realized, where a control object output corresponding to a delayed detection can be attained with few calculations. Next, an embodiment to attain the twelfth object of this invention will be described with reference to FIG. 32. In this diagram, the numeral 131 indicates the predictive control apparatus for this invention which takes the future target instruction, the output from the control object 132 and the model information from the identifier 133 as its input, and outputs a control input to the control target 132. As for the method for deciding the control input, for example, the method in the first embodiment could be used for the case where model information taken as the step response model is inputted, or the method in the eleventh embodiment could be used for the case where a transfer function model is inputted, or otherwise a step response could be calculated from of the inputted transfer function model and the method for the first embodiment could be used. Usual methods, such as, transitional response methods, frequency response methods, statistical techniques, time series expression methods and standard form model reference adaptive identifiers can be used as the identification method for the identifier 133 (Ohm Co. [automatic control foundations handbook], P71-90, P702). FIG. 33 is a view of a further embodiment to attain the twelfth object. In this diagram, the numeral 141 indicates the predictive control apparatus for this invention, which inputs a future target instruction, the output from the control object 142 along with the target model, and outputs a control input to the adaption controller 144. A method the same as the one used for the embodiment in FIG. 32 set according to the form of the target model is suitable as the method of setting the control input. The numeral 144 indicates the adaption controller which takes the target model, the control input and the control object output as its input, and which controls the control target by having its characteristic from the control input to the output coincide with the target model characteristic. In this way the identifier 133 can then be realized by a method such as adjusting the gain by making a comparison between the identifier model obtained by the same technique and the target model, by a model reference adaptive control method or by a torque observer. The identifier 133 and the adaption controller 144 could also obtain signals other than control input and output signals from the preview control apparatus 131 and 141 or the control objects 132 and 142. Also, the identifier 133 and the adaption controller 144 could be constructed internally within the predictive controllers 131 and 141 or the control targets 132 and 142 respectively. In the method described above, according to the present invention, apredictive control apparatus with a more accurate tracking operation which responds to fluctuations in the characteristics of the control object can be realized. Next, an embodiment to attain the thirteenth object of this invention will be described with reference to FIG. 34. In this diagram, the numeral 151 indicates the predictive control apparatus for this embodiment which inputs at the current time i a future target position instruction increment Δr(i+M), and a control object position output increment Δy(i-K) which was sampled K (where K is greater then or equal to zero) samples previously, and then inputs a speed instruction v(i) to the speed controller. Here, Δ expresses the increment during sampling periods. Numeral 152 indicates the memory for storing the future target position instruction increment Δr(i+m), numeral 153 indicates the memory for storing the constants v m , Q 0 , Q 1 , g n and E, numeral 154 indicates the memory for storing the past speed instruction v(i-n) and numeral 155 indicates the memory for storing the control object position output increment Δy(i-K-1). Also, numeral 156 indicates the arithmetic unit where the speed instruction v(i) is calculated according to the equation: The numeral 157 indicates the subtracter which obtains the position deviation increment Δe(i+K) from the target position instruction increment Δr(i-K) and the control object position output increment and numeral 158 indicates the integrator which obtains the position deviation e(i-K). Here, deductions are made from equation (13-1). The first N samples {H 1 , H 2 , ..., H N } for the control object position output increment at the time of the inputting of the unit step instruction to the speed controller are sampled, and from that point on if it is assumed that the increment h n =H n -H n-1 reduces at a constant rate P, a control target model can be obtained from the next equation. Here, the future position output increment occurring at a time i can be estimated from Δy*(i+m) = Δ y (i+m)+Δy(i-K)-Δ y (i-K) or Δy*(i+m) = Δ y (i+m) Now, a description will be given for the case where an estimate is obtained from equation (13-3). If the future speed instruction v(i+m)=0 (m=1, 2, ...), the estimate Δy*(i+m) for the future position output increment according to equations (13-2) and (13-3) can be estimated as being: where h N+j =P j h N (j≥1). Moreover, if Δy and is replaced with Δy this becomes: It follows that the future position deviation increment estimate Δe*(i+m) is given by the following equation: Δe*(i+m) = Δr(i+m)-Δy*(i+m) and if the speed instruction v(i) is selected so that the evaluation function becomes a minimum, by putting ∂J/∂u(i)=0, equation (13-1) can be obtained. Each of the constants v m , Q 0 , Q 1 , g n and E can then be given by the next equations. where h j =0 (j≤0), h N+j =P j h N (j≥1) An actual measured value or a value simulated from an item such as the transmission function for the control object can be used as the sample series for the control object position output increment Δy at the time the step instruction is inputted into the speed controller for the control object. Also, for the case where the future position output increment is estimated from the equation (13-4), each of the constants v m , Q 0 , Q 1 , g n and E can be given by the following equations. Where h j =0 (j≤0), h N+j =P j h N (j≥1) By substituting into equation (13-1) the following equation can be obtained. In this case, the constant for predictive control g n becomes, from equation (13-9), and from equation (13-10) becomes: The case where the control object speed output coincides with the target speed instruction can be thought of as being exactly the same as the case where control object position output coincides with the target position instruction. The speed instruction increment taken as the target instruction and the speed output increment is taken as the control object output. An actual measurement is taken of the control object speed output increment Δy at the time of inputting the step instruction to the control object acceleration or torque controller, or obtained by simulation from the transfer function or, in the same way, could be deduced from the acceleration or torque instruction in the control equation. As mentioned above, according to the present embodiment, a predictive control apparatus can be realized where calculations can be carried out in less time while less memory is required to perform these calculations. Next, an embodiment to attain the fourteenth object of this invention will be described with reference to FIG. 35. In this diagram, the numeral 161 indicates the predictive control apparatus for this embodiment which inputs at the current time i a future target instruction increment Δr(i+M), and a control object position output increment Δy(i-K) sampled K (K≥0) samples previously, and then outputs a control input u(i) to the control object. Here, Δ expresses the increment during sampling periods. Numeral 162 indicates the memory for storing the target instruction increment Δr(i+m), numeral 163 indicates the memory for storing the constants v m , Q 0 , Q 1 , g n and E, numeral 164 indicates the memory for storing the past control input increment Δu(i-n) and numeral 165 indicates the memory for storing the control object output increment Δy(i-K-1). Also, numeral 166 indicates the arithmetic unit where the control input increment Δu(i) is calculated according to the equation: The numeral 167 indicates the integrator which obtains the control input u(i) from Δu(i), numeral 168 indicates the subtracter which obtains the deviation increment Δe(i-K) from the target instruction increment Δr(i-K) and the control object output increment Δy(i-K) and numeral 169 indicates the integrator which obtains the deviation e(i-K) from Δe(i-K). Here, deductions are made from equation (14-1). The first N samples {H 1 , H 2 , ..., H N } for the control object unit step response are sampled, and from that point on if it is assumed that the increment h n =H n -H n-1 reduces at a constant rate P, a control object model can be obtained from the next equation. Here, the future output increment occurring at time i can be estimated by the following equation. Δy*(i+m) = Δ y (i+m)+Δy(i-K)-Δ y (i+K) By making Δu(i+m)=0 (m=1, 2, ...) the future output increment estimate Δy*(i+m) according to equations (14-2) and (14-3) can be estimated by: where h N+j =P j h N (j≥1). Further, replacing Δy and with Δy this becomes: It follows that the future deviation increment estimate Δe*(i+m) is given by the following equation: Δe*(i+m) = Δr(i+m)-Δy*(i+m) and if the control input increment Δu(i) is selected so that the evaluation function becomes a minimum, by putting ∂J/∂u(i)=0 equation (14-1) can be obtained. Each of the constants v m , Q 0 , Q 1 , g n and E can then be given by the next equations. where h j =0 (j≤0), h N+j =P j h N (j≥1) An actual measured value or a value simulated from the transfer function for the control object can be used as the control object step response sample series. As mentioned above, according to the present embodiment, a predictive control apparatus can be realized where calculations can be carried out in less time while less memory is required to perform these calculations. Next, an embodiment to attain the fifteenth object of this invention will be described with reference to FIG. 36. In this diagram, the numeral 171 indicates the predictive control apparatus for this embodiment which inputs at the current time i a future target speed instruction r(i+M), and a control object speed output y(i-K) sampled K (where. K≥0) samples previously, and then outputs an acceleration or torque instruction u(i) to the acceleration or torque controller. Numeral 172 indicates the memory for storing the target speed instruction r(i+m) (m=-K, -K+1, ..., M), numeral 173 indicates the memory for storing the constants q m , Q, g n and E and numeral 174 indicates the memory for storing the past acceleration or torque instruction u(i-n). Also, numeral 175 indicates the arithmetic unit where the acceleration or torque instruction u(i) is calculated according to the equation: The numeral 176 indicates the subtracter which obtains the speed deviation e(i-K) from the target speed instruction r(i-K) and the control object speed output y(i-K). Here, deductions are made from equation (15-1). The control object speed output can be shown to be y (i-K)=H 1 u(i-K-1)+H 2 u(i-K-1)+···+H N u(i-K-N)+··· where H j (j=1, 2, ..., N) is the increment for the control object speed output y(i) at the time of inputting the unit step instruction into the accelerator or torque controller. Here, the future speed output occurring at time i can be estimated from the following equation. y*(i+m)= y (i+m)+y(i-K)- y (i-K) If the future speed output u(i+j) (j=1, 2, ...) is made equal to zero, the equation can be acquired from equation (15-2) and equation (15-3), where n is chosen to be sufficient for H j to become constant. It follows that the future speed deviation increment e*(i+m) can be shown to be and if the acceleration or torque instruction u(i) for this time is chosen so that the evaluation function becomes a minimum, by putting ∂J/∂u(i)=0, equation (15-1) can be obtained. Each of the constants q m , Q, g n and E can then be given by the following equations. An actual measured value or a value simulated from an item such as the transmission function for the acceleration or torque controller and the control object can be used with the unit step response sample series for the accelerator or torque controller and the control object. Next, a case where data increments are handled will now be described. In this case, the future target speed instruction increment taken to be the target instruction and the speed output increment taken to be the control object speed output are inputted, and the future speed output increment Δy*(i+m) can be estimated by Then, if the transfer function or is made to become a minimum, by setting ∂J/∂Δu(i)=0, the acceleration or torque instruction increment Δu(i) can be obtained from the next equation, and u(i) can then be obtained by integration. whereby for the case where the evaluation function in equation (15-9) is used, the constants for the predictive control can be given by the following equations. Also, for the case where equation (15-10) is used as the evaluation function, in equation (15-12) F will become zero. In the above, instead of using the equation (15-8) to estimate the future speed output increment Δy*(i+m), the estimation method in the next equation could be used: where h m =H m -H m-1 . Then, by making the evaluation function in equation (15-9) and equation (15-10) a minimum and then setting ∂J/∂u(i)=0, the acceleration or torque instruction u(i) can be obtained from the following equations. or In these equations, using the evaluation function in equation (15-9) the constants for predictive control by equation (15-14) are given to be and in the same way by equation (15-15) are given to be Also, when using the evaluation function in equation (15-10), g 1 =g 1 +c/W can be used in equation (15-16) and (15-17), respectively. When u(i) has been decided in the above method it can then be output to the acceleration or torque controller for the control object. In the above method, according to this embodiment, a predictive control apparatus can be realized which can be applied to a case where speed control is carried out by obtaining an acceleration or torque instruction from a speed instruction. Next, an embodiment to attain the sixteenth object of this invention will be described with reference to FIG. 37. In this diagram, the numeral 181 indicates the predictive' control apparatus for this embodiment which inputs at the current time i a future target instruction increment Δr(i+M), and a control object output increment Δy(i-K) which was sampled K (where K≥0) samples previously, and then outputs a control input u(i) to the control object. Numeral 182 indicates the memory for storing the target instruction increment Δr(i+K), ..., Δr(i+M), numeral 183 indicates the memory for storing the constants E, v 1 , ..., v M , p 0 , ..., p Na-1 , g 1 , ..., g Nb+K-1 , numeral 184 indicates the memory for storing the output increment Δy(i-K), ..., Δy(i-K-Na+1) and numeral 185 indicates the memory for storing the past control input increment Δu(i-1), ..., Δu(i-K-Nb+1). The numeral 187 indicates the subtracter which obtains the deviation increment Δe(i-K) from the target instruction increment Δr(i-K) stored in the memory 182 and the control object output increment Δy(i-K) and numeral 188 indicates the integrator which obtains the deviation e(i-K) from Δe(i-K). Also, numeral 186 indicates the arithmetic unit where the control input increment Δu(i) is calculated according to the equation: The calculated Δu(i) is stored in the memory 185 and input to the integrator 189. The control input u(i) obtained by the integrator 189 is then output to the control object as an output from the predictive control apparatus 181. Here, deductions are made from equation (16-1). Now, if the control object transform function model is obtained by the dispersion time model G p (Z) = b 1 z -1 +···+b Nb z -Nb 1-a 1 z -1 -···-a Na z -Na this input/output increment model becomes the following equation. where Δ indicates the sampling period increment. At a time i, as an actual measured value Δy(i-n) (n≥K) can be obtained for the output increment up until the time i-K, by using an actual measured value for the output increment from this point on to estimate the output increment estimate Δy*(i+m) becomes the following equation. Here, by making the future control input increment Δu(j)=0 (j>i), the coefficients A mn and B mn can be given by: A (-K+1)n = a (n-K+1) m=-K+1, K≤n≤Na+K-1 B (-K+1)n = b (n-K+1) m=-K+1, 0≤n≤Nb+K-1 a n =0 (n>N a ), b n =0 (n<1 and n>N b ) Here, the future deviation estimate e*(i+m) is given by: so if the control input increment Δu(i) is selected so that the evaluation function becomes a minimum, by making ∂J/∂Δu(i)=0 equation (16-1) can be obtained. Each of the constants V m , p n , E and g n are given by the following equations. Also, by replacing the transfer function in equation (16-8) with equation (16-1) becomes : It follows that for this case, the past control input u(i-1) and the constant F=c/W can be stored in the memory, and Δu(i) can then be calculated according to equation (16-1'). In the above method, according to this embodiment, a predictive control apparatus can be realized which has a more accurate tracking function corresponding to the late detection of a control object output while the carrying out of this function requires less memory and fewer calculations. With the predictive control apparatus described above, a filter can be used in the target instruction in order to apply an impulse to the control object. For example, if the method in Japanese Patent Application No. 4-59677 is used, the filtered target instruction is: where f j is given to be the (2k f +1)th filter coefficient and the right hand side of the control signal deciding equation in Claim 3, 5, 14, 23, 26 or 39, becomes where The right hand side of the control signal deciding equation in Claim 7, 10, 12, 16, 18, 34, 35, 36, 37, 38, 40, 41, 42, 43 or 44: becomes where INDUSTRIAL APPLICABILITY This invention can be applied to the control of items such as construction machinery and robots.

Claims: A predictive control apparatus for deciding a control signal for a control object (9, 18, 26, 36, 56, 62, 63, 82, 83, 132, 142, 144) every predetermined sampling period in such a manner that a future deviation estimate obtained from a model of the control object (9, 18, 26, 36, 56, 62, 63, 82, 83, 132, 142, 144) becomes minimal so that the output of the control object (9, 18, 26, 36, 56, 62, 63, 82, 83, 132, 142, 144) coincides with a target signal, characterized by: means for inputting a future target signal (1, 11, 21, 31, 51), means for storing said future target signal (2, 12, 22, 32, 52, 71, 91, 102, 112, 122, 152, 162, 172, 182), means for storing a past control signal (4, 14, 24, 34, 54, 73, 93, 94, 104, 115, 125, 154, 164, 174, 185), means for storing a predictive control constant (3, 13, 23, 33, 53, 72, 92, 103, 113, 123, 153, 163, 173, 183), means for obtaining a tracking deviation (5, 15, 28, 29, 35, 39, 40, 58, 59, 74, 95, 107, 108, 116, 117, 126, 127, 156, 157, 158, 166, 168, 169, 175, 176, 186, 187, 188) according to the difference between the future target signal and said control object output, and means for calculating a control signal (5, 15, 25, 35, 55, 74, 95, 105, 116, 126, 156, 166, 175, 186) to be outputted to said control object (9, 18, 26, 36, 56, 62, 63, 82, 83, 132, 142, 144) such that an evaluation function including at least the squared weighting of the future deviation estimate becomes minimal, according to the future target signal, the output of the control object (9, 18, 26, 36, 56, 62, 63, 82, 83, 132, 142, 144), the past control signal, the deviation and the model of the control object (9, 18, 26, 36, 56, 62, 63, 82, 83, 132, 142, 144). A predictive control apparatus according to claim 1, which takes as its input a future target instruction r(j) and a control object output x(i) and outputs to a control object(9) a control input u(i) so that the output x(i) of the control object (9) coincides with a target instruction, characterized in that the means for storing a future target instruction (2) r(i+m) (m=1, 2, ..., M), the means for storing a past control input (4) u(i-n) (n=1, 2, ..., N-1), and the means for deciding a control input (5) u(i) cooperate so that an evaluation function relating to a future deviation estimate e*(i+m) (m=1, 2, ..., M) and W m being the weighting coefficient for the future deviation estimate e*(i+m) obtained from a model where a control object output is made to be with b n = h n - Ph n-1 and where H n is the unit step response for the sample, h n =H n -H n-1 and P is the damping ratio for h n (n>N), becomes a minimum. A predictive control apparatus according to claim 2, characterized in that a control input u(i) is decided according to with the predictive control constants q m (m=1,2,...,M), Q and g n (n=1, 2,..., N-1) A predictive control apparatus according to claim 1, which takes as its input a future target instruction r(j) and a control object output x(i) and outputs to a control object (18) a control input u(i) so that the output of the control object (18) coincides with a target instruction, characterized in that the means for storing a future target instruction (12) r(i+m) (m=1, 2, ..., M), the means for storing a past control input (14) u(i-n) (n=1, 2, ..., N-1) and the means for deciding a control input (15) u(i) cooperate so that an evaluation function relating to a future deviation estimate e*(i+m) (m=1, 2, ..., M) and W m being the weighting coefficient for the future deviation estimate e*(i+m) obtained from a model where a control object output is made to be when h n is the step response sample increment, becomes a minimum. A predictive control apparatus according to claim 4, characterized in that a control input u(i) is calculated according to with the predictive controls constants q m (m=1,2,...,M) and g n (n=1,2,...,N-1). A predictive control apparatus according to claim 1, which takes as its input a future target instruction increment Δr(j) and a control object output increment Δy(i) and outputs to a control object (26) a control input u(i) or its increment Δu(i) so that the output of the control object (26) coincides with a target instruction, characterized in that the means for storing a future target instruction increment (22) Δr(i+m) (m=1, 2, ..., M), the means for storing a past control input increment (24) Δu(i-n) (n=1, 2, ..., N-1), the means for obtaining deviation (28, 29) e(i) and the means for deciding a control input increment (25) Δu(i) cooperate so that an evaluation function where Δ is the increment operation during sampling periods, relating to a future deviation increment estimate Δe*(i+m) and deviation e(i) and W m being the weighting coefficient for the future deviation estimate e*(i+m) obtained from information relating to the motion characteristics of the control target, becomes a minimum. A predictive control apparatus according to claim 6, characterized in that and a control input increment Δu(i) is decided according to with predictive control constants v m (m=1,2,...,M), Q, q n (n=1,2,...,N-1) and E. A predictive control apparatus according to claim 1, which takes as its input a future target instruction increment Δr(j) and a control object output increment Δy(i-K) which was sampled K (K≥0) samples previously and outputs to a control object (36) a control input u(i) or its increment Δu(i) so that the output of the control object coincides with a target instruction, characterized in that the means for storing a target instruction increment (32) Δr(i+m) (m=-K-1, ..., M), the means for storing a past control input increment (34) Δu(i-n) (n=1, 2, ..., N+K-1), the means for obtaining the deviation (40) e(i-K) and means for deciding a control input increment (35) Δu(i) cooperate so that an evaluation function relating to a future deviation increment estimate Δe*(i+m) and deviation e(i-K) and W m being the weighting coefficient for the future deviation estimate e*(i+m) obtained from information relating to the motion characteristics of the control target, becomes a minimum. A predictive control apparatus according to claim 8, characterized in that an evaluation function is made to be C being a constant. A predictive control apparatus according to claim 8 or 9, characterized in that a control input increment Δu(i) is decided according to with the predictive control constants v m (m=-K+2,...,M), Q, g n (n=0, 1,..., N+K-1) and E. A predictive control apparatus according to claim 1, which takes as its input a future target instruction increment Δr(j) and a control object output increment Δy(i-K) sampled K (K≥0) samples previously and output to a control object (56) a control input u(i+1) or its increment Δu(i+1) so that the output of the control object (56) coincides with a target instruction characterized in that the means for storing a target instruction increment (52) Δr(i+m) (m=0,1, ..., M), the means for storing a past control input increment (54) Δu(i-n) (n=0,1, ..., N-1), the means for obtaining the deviation (59) e(i-K) and the means for deciding a control input increment (55) Δu(i+1) cooperate so that an evaluation function with a constant C relating to a future deviation increment estimate Δe*(i+m) and deviation e(i-K) and W m being the weighting coefficient for the future deviation estimate e*(i+m) obtained from information relating to the motion characteristics of the control target, becomes a minimum. A predictive control apparatus according to claim 11, characterized in that a control input increment Δu(i+1) is decided according to with the predictive control constants v m (m=1,2,...,M), Q, g n (n=0,1,...,N-1) and E. A predictive control apparatus (61) according to claim 1, which takes as its input a future target instruction r(j) and a control object ouput y(i) and outputs to a control object speed controller (62) a speed instruction v(i) so that the output of the control object coincides with a target instruction characterized in that the means for storing a future target instruction (71) r(i+m) (m=0,1, ..., M), the means for storing a past speed instruction (73) v(i-n) (n=1,2,..., N-1) and the means for deciding a speed instruction (74) v(i) cooperate so that an evaluation function relating to a future deviation estimate e(i) and increment estimate e*(i+m), W m being the weighting coefficient for the future deviation estimate e*(i+m) and the speed instruction v(i) obtained from information relating to the motion characteristics of the control target with C being a constant, becomes a minimum. A predictive control apparatus according to claim 13, characterized in that a speed instruction v(i) is decided according to with the predictive control constants q m (m=1,2,...,M), Q, g n (n=1,2,...N-1) and E. A predictive control apparatus (81) according to claim 1, which takes as its input a future target instruction increment Δr(j) and a control object output increment Δy(i) and outputs to a control object speed controller (82) a speed instruction v(i) so that the output of the control object coincides with a target instruction characterized in that the means for storing a future target instruction increment (91) Δr(i+m) (m=0,1,...,M), the means for storing a past speed instruction increment (93) Δv(i-n) (n=1,2,...,N-1), the means for storing a speed instruction (94) v(i-1) and the means for deciding a speed instructions increment (95) v(i) cooperate so that an evaluation function relating to a future deviation increment estimate Δe*(i+m) and deviation e(i), W m being in the weighting coefficient for the future deviation estimate e*(i+m) and the speed instruction v(i) obtained from information relating to the motion characteristics of the control target with A and C being constants, becomes a minimum; and means for integrating (96) Δv(i) is provided to produce a speed instruction v(i). A predictive control apparatus according to claim 15, characterized in that a speed instruction Δv(i) is decided according to with the predictive control constants (92) v m (m=1,2,...,M), Q, g n (n=1,2,...,N-1), E and F. A predictive control apparatus (101) according to claim 1, which takes a future time target instruction and a control object output as its inputs and outputs a control input to a control object so that the output of the control object having two constituent integral portions between its input and output coincides with a target instruction, characterized in that the means for obtaining a present time control input (105) u(i) decides from a future target instruction increment Δr(i+m) (m=0,1,...,M) and a control object output increment Δy(i) where v m (m=1,2,...,M), Q, g n (n=1,2,...,N-1) and E are predictive control constants and Δ is the increment calculation made during sampling periods by making the evaluation function relating to a deviation estimate e* until M sample periods in the future, a current deviation e(i) and a control input u(i) and W m being the weighting coefficient for the future deviation estimate e*(i+m) with C being a constant, and to become a minimum. A predictive control apparatus according to claim 17, further comprising a motor and controller for a control object to take a torque instruction or acceleration instruction as input and output a position. A predictive control apparatus according to claim 17, further characterized in that predictive control constants are obtained by taking values simulated from, for example, the real side of the incremental series for the unit step response of the control object or the transfer function of the control object as information which relates to the drive characteristics of the control object. A predictive control apparatus according to claim 17, characterized in that predictive control constants are obtained by taking the gain and sampling period of the control object as information, which relates to the drive characteristics of the control object. A predictive control apparatus according to claim 17, characterized in that predictive control constants are obtained by taking the gain of a torque controller, the inertia of a motor and a load and the sampling period as information, which relates to the drive characteristics of the control object. A predictive control apparatus (111) according to claim 1, which takes as its input with respect to a current time i a target instruction r(i+M) for M samples in the future and a control object output y(i-K) for K (K>0) samples previously and outputs a control input u(i) to a control object so that the output of a control object coincides with a target instruction, characterized in that the means for storing a target instruction (112) r(i+m) (m=-K,...,M), the means for storing a predictive control constant (113) q m (m=1,2,...,M), Q 0 , Q 1 , g n (n=1,2,...,N+K) and E, the means for storing a past control input (115) u(i-n) (n=1,2,...,N+K), the means for obtaining a deviation (117) e(i-K) from the difference between a target instruction and a control object output and the means for obtaining a current time control input (116) cooperate with means for storing a past object output (114) y(i-K-1) to decide A predictive control apparatus (121) according to claim 1, which takes as its input with respect to a current time i a target instruction r(i+M) for M samples in the future and a control object output y(i-K) for K (K≥0) samples previously and outputs a control input u(i) to a control object so that the output of a control object having a transfer function model G p ( Z ) = b 1 z -1 +...+ b Nb z -Nb 1- a 1 z -1 -...- a Na z -Na coincides with a target instruction, characterized in that the means for storing a target instruction (122) r(i+m) (m=M,M-1,... -K), the means for storing a predictive control constant (123), the means for storing a past control input (125) u(i-1) (n=1,2,...,K+N b ), the means for obtaining a deviation (127) e(i-K) from the difference between a target instruction r(i+m) and a control object output y(i-K) and the means for deciding a current time control input (126) u(i) cooperate with a means for storing a past object output (124) y(i-k) (k=1,2,...K+Na) so that a transfer function relating to a deviation estimate e* for M sampling periods into the future, W m being the weighting coefficient for the deviation estimate e*, the deviation e(i-K) and the control input u(i), becomes a minimum according to the deviation, future target instruction, past output, past control input and the transfer function for the control object, with α and C being constants. A predictive control apparatus according to claim 23, characterized in that the evaluation function is made to be A predictive control apparatus according to claim 23 and claim 24, further characterized in that the means for deciding a current control input (126) outputs u(i) according to where q m (m=1,2,...,M), p n , E and g n (n=1,2,...,Nb+k) are predictive control constants. A predictive control apparatus (131) according to claim 1, characterized in that the predictive control apparatus (131) receives signals from the means for inputting a future time target instruction, the controller (132) and from a control object model identifier (133) as model information. A predictive control apparatus according to claim 26, characterized in that the model identifier (133) for the control object is internal. A predictive control apparatus according to claim 1, characterized in that there is an adaption controller (144) between the predictive control apparatus (141) and the control object (142) such that the output from the control object (142) is controlled according to a set up target model and the adaption controller (144) which moves in the same way as this target model coincides with a target instruction. A predictive control apparatus according to claim 28, characterized in that the adaptive controller (144) for controlling the control object (142) moving in the same way as a set up target model is internal. A predictive control apparatus according to one or more of claims 26 to 29, characterized by further comprising means for inputting a step response model taken as model information or a target model. A predictive control apparatus according to one or more of claims 26 to 29, characterized by further comprising means for inputting a transfer function model taken as model information or a target model. A predictive control apparatus according to claim 31, characterized by further comprising means for calculating a step response from the inputted transfer function by simulation. A predictive control apparatus (151) according to claim 1, which takes as its input with respect to a current time i, a target position instruction increment or a target speed instruction increment Δr(i+M) for M samples in the future and a control object position output increment or a control object speed output increment Δy(i-K) for K (K≥0) samples previously and outputs a speed instruction or an acceleration or torque instruction v(i) to the control object so that the position output or the speed output of the control object coincides with a target instruction or a target speed instruction, characterized in that the means for storing a future target signal is a means for storing a target position instruction increment or a target speed instruction increment (152) Δr(i+m) (m=M, M-1,.., -K), the means for storing a past control signal is a means for storing a past speed instruction or a past acceleration or torque instruction (154) v(i-n) (n=1,2,...,N+K) and there is a means for storing a past control object position output increment or a past control object speed output increment (155) Δy(i-K-1); the means for obtaining a deviation is a means for obtaining a position deviation or a speed deviation (157, 158) e(i-K); and the means for calculating a control signal is a means for obtaining a current time speed instruction or a current time acceleration or torque instruction (156) v(i) according to where v m (m=-K+1,...,M), Q 0 , Q 1 , g n (n=1,2,...,N+K) and E are predictive control constants and e is the position deviation or the speed deviation. A predictive control apparatus (151) according to claim 33, characterized in that a means for storing a past control object position output increment or a past control object speed output increment (155)Δy (i-K-1) is comprised and the means for calculating a control signal (156) v(i) outputs A predictive control apparatus (161) according to claim 1, which takes as its input with respect to a current time i a target instruction increment Δr(i+M) for M samples in the future and a control object output increment Δy(i-K) for K (K≥0) samples previously and outputs a control input u(i) to the control object so that the output of the control object coincides with a target instruction, characterized in that the means for storing a future target signal is a means for storing a target speed instruction increment (162) Δr(i+m) (m=M, M-1,.., -K), the means for storing a past control signal is a means for storing a past control input (164) u(i-n) (n=1,2,...,N+K) and there is a means for storing a past control object output increment (165) Δy(i-K-1); the means for obtaining a deviation is a means for obtaining a deviation (168, 169) e(i-K); and the means for calculating a control signal is a means for obtaining a current control input increment (166) Δu(i) according to where v m (m=-K+1, ...,M) Q 0 , Q 1 , g n (n=1,...,N+K) and E are predictive control constants and e is the deviation. A predictive control apparatus (171) according to claim 1, which takes as its input with respect to a current time i a target speed instruction r(i+M) for M samples in the future and a control object speed output y(i-K) for K (K≥0) samples previously and outputs an acceleration or torque instruction u(i) to a control object acceleration or torque controller so that the speed output of the control object coincides with a target speed instruction, characterized in that the means for storing a future target signal is a means for storing a target speed instruction (172) r(i+m) (m=-K,..., M) and the means for storing a past control signal is a means for storing a past acceleration or torque instruction (174) u(i+n) (n=-1,...,-N-K); the means for obtaining a deviation is a means for obtaining a speed deviation (176) e(i-K); and the means for calculating a control signal is a means for obtaining a current acceleration or torque instruction (175) u(i) from the inputted target speed instruction r(i+M) and control object speed output y(i-K) according to where q m (m=1,... ,M), Q, g n (n=1,...,M+K-1) and E and F are predictive control constants. A predictive control apparatus (171) according to claim 1 which takes as its input with respect to a current time i a target speed instruction increment Δr(i+M) for M samples in the future and a control object speed output increment Δy(i-K) for K (K≥0) samples previously and outputs an acceleration or torque instruction u(i) to a control object acceleration or torque controller so that the speed output of a control object coincides with a target speed instruction, characterized in that the means for storing a future target signal is a means for storing a target speed instruction increment Δr(i+m) (m=-K,..., M) and the means for storing a past control signal is a a means for storing a past acceleration or torque instruction u(i+n) (n=-1,...,-N-K); the means for obtaining a deviation is a means for obtaining a speed deviation (176) e(i-K); and the means for calculating a control signal is a means for obtaining a current acceleration or torque instruction increment Δu(i) according to where v m (m=-K+1, ...,M), Q, g n (n=1, ...,N+K-1), E and F are predictive control constants; and a means for integrating Δu(i) to give an acceleration or torque instruction u(i) is provided, or the means for calculating a control signal is a means for obtaining a current acceleration or torque instruction u(i) according to where v m (m=-K+1,...,M), Q, g n (n=1,...,N+K-1) and E are predictive control constants, or the means for calculating a control signal is a means for obtaining a current acceleration or torque instruction u(i) according to where V m (m=-K+1,...,M), Q, g n (n=1,...,N+K-2) and E are predictive control constants. A predictive control apparatus (181) according to claim 1, which takes as its input with respect to a current time i a target instruction increment Δr(i+M) for M samples in the future and a control object output increment Δy(i-K) for K (K≥0) samples previously and outputs a control input u(i) to a control object so that the output of the control object having a transfer function model G p ( z ) = b 1 z -1 +...+ b Nb z -Nb 1- a 1 z -1 -...- a Na z -Na coincides with a target instruction, characterized in that the means for storing a future target signal is a means for storing a target instruction increment (182) Δr(i+m) (m=-K,..., M), the means for storing a past control signal is a means for storing a past output increment (184) Δy(i-n) (n=1, ..., N+Na-1) and there is a means for storing a past control input increment (185) Δu(i-n) (n=1, ...,K+Nb-1); the means for obtaining a deviation (188) e(i-K) obtains the deviation from a target instruction increment and a control object output increment; the means for calculating a control signal is a means for deciding a current time control input increment (186) Δu(i) according to where v m (m=-K+1,...,M), p n (n=0,...,Na-1), E and g n (n=1,...,Nb+K-1) are predictive control constants; and a means for obtaining a control input (189) u(i) from Δu(i) is provided. A predictive control apparatus according to claim 38, characterized in that: the means for deciding a control input increment (186) Δu(i) calculates where V m (m=-K+1,...,M) p n (n=0,...,Na-1), E, g n (n=1,...,Nb+K-1) and F are predictive control constants. A predictive control apparatus according to claim 3, 5, 14, 22, 25 or 36, characterized in that the right hand side of the equation for deciding the control signal becomes where f n =0 (n<-k f , n>k f ) and f n is the (2k f +1)nth filter coefficient. A predictive control apparatus according to claim 7, 10, 12, 16, 17, 33, 34, 35, 37, 38 or 39, characterized in that the right hand side of the equation for deciding the control signal becomes where f n =0 (n<-k f , n>k f ) and f n is the (2k f +1)nth filter coefficient

Inventors: NAKAMURA HIROSHI, TSURUTA KAZUHIRO, NAKAMURA, HIROSHI,, TSURUTA, KAZUHIRO,, NAKAMURA, HIROSHI, TSUKUBA LABORATORY, TSURUTA, KAZUHIRO, TSUKUBA LABORATORY
Applicants: YASKAWA DENKI SEISAKUSHO KK, KABUSHIKI KAISHA YASKAWA DENKI
Application year: 1993
Main classification: G05B13
IPC codes: G05B19, G05B13