VEHICLE CONTROL SYSTEM

Abstract: A vehicle control system embodied as an autocruise control. Fuzzy Logic is introduced in the system such that an expert driver's operation or judgment made on a non-automated vehicle is able to be incorporated in the system as fuzzy production rules. In the system, the control is effected in accordance with the rules which can be expressed linguistically in a simple manner and have a predictive nature therein. An operating condition of the vehicle is detected through parameters such as a vehicle speed(VDIF), a vehicle acceleration (α) and a change of vehicle acceleration (Δα) which are then assigned on a scale named universe of discourse. The satisfaction degree is evaluated so that one rule is selected (S412). A control value is determined from the selected rule and is output to an acutuator to be controlled.

Description: This invention relates to a vehicle control system and more particularly to a vehicle control system which opens the way to the incorporation in the control of such non-physical factors as human judgments and impressions. Vehicle devices and mechanisms that formerly were operated manually by the vehicle driver are, more and more, being controlled automatically. An example of such automation can be seen for instance, in the technology described in Japanese Patent Publication No. 59(1984)-9740, which discloses an autocruise control system. Such conventional automatic control has employed various techniques including proportional control, proportional-plus-integral control and proportional-plus-integral-plus-differential control. In all cases, however, the control techniques have been premised on receiving physical quantities as input values. The physical quantities are represented numerically and input to the control system as parameters indicating states of vehicle operation, whereafter control values are computed in accordance with an appropriate manner. In this kind of control, however, it is impossible to include among the control factors things that are difficult to express clearly as physical quantities, such as the feelings and judgments of a human being. Thus the conventional control systems are unable to incorporate into the automatic control the manual driving techniques which a skilled driver uses on a non-automated vehicle to realize safety and economy as well as riding comfort and enjoyment. One of the reasons for this is that, as stated above, the feelings, judgments, impressions and the like of the skilled driver cannot be expressed as physical quantities and thus cannot be used as control factors in the systems. Such a skilled driver operates an accelerator pedal and a shift lever or the like during driving of his vehicle while making judgments with regard to the constantly changing surrounding circumstances, and in the process of doing this makes predictions regarding circumstances apt to arise in the future. With the conventional control systems, however, there is no hope of being able to incorporate such predictive judgments into the automatic control. For the same reason, the conventional control techniques are unable to respond to the difference or diversity in feelings among individual drivers. As a result, it has not been possible to adequately give each individual driver a feeling of oneness with the vehicle, i.e. to give him the feeling that the vehicle is an extension of himself. Moreover, in the conventional control system, the control techniques have become increasingly complicated and detailed year after year. Thus, when a microcomputer is used in the system, it is disadvantageously necessary to employ a memory of large capacity. SUMMARY OF THE INVENTION It is therefore an object of the invention to provide a vehicle control system which overcomes the drawbacks of the prior art. Another object of the invention is to provide a vehicle control system which opens the way to higher precision control techniques by incorporating in the control not only human feelings, judgments and other factors which cannot be easily expressed as physical quantities, but also the predictive judgments of an expert driver made during manual operation on a non-automated vehicle. Still another object of the invention is to provide a vehicle control system which, by opening the way to the possibility of reflecting the difference and diversity in the feelings of individual drivers without need to make changes in control principles, makes it possible to enhance the feeling of each individual driver that the vehicle is an extension of himself. A further object of the invention is to provide a vehicle control system which, by enabling fine control to be expressed by simple control principles, makes it possible to minimize the memory capacity required when carrying out the control using a microcomputer. Documents of relevance to the present application include EP-A2-92832 and IEEE SPECTRUM, AUGUST 1984, pages 26 to 32 (Zadeh). From PROCEEDINGS IECON '84, Vol.2, October 22-26, 1984, pages 657-662 (S. Yasunobu et al) there is known a vehicle control system according to the precharacterising portion of Claim 1. The present invention is characterised in that said vehicle control system is an automobile cruise control system for keeping the travelling speed of the vehicle at a predetermined speed; said output command is expressed as a term of a fuzzy membership function; and said second means is adapted to convert the output command determined into a real number control value in advance of being provided to said third means. BRIEF DESCRIPTION OF THE DRAWINGS These and other objects and advantages of the invention will be more apparent from the following description of examples of the invention with reference to the drawings, in which: Figure 1 is an explanatory graph illustrating theoretical premises of Fuzzy Logic applied in a vehicle control system according to the invention; Figure 2 is a flowchart showing preparatory procedures in designing a vehicle control system according to the invention which enables the application of Fuzzy Logic in Figure 3 is a flowchart illustrating routine procedures to be prosecuted in an actual operation according to the designed vehicle control system; Figure 4 is an overall schematic view of a vehicle control system embodied as an autocruise control system; Figure 5 is a block diagram of a control unit in the system of Figure 4; Figure 6 is a flowchart similar to that of Figure 2 and shows preparatory procedures in designing the autocruise control system illustrated in Figure 4; Figure 7 is a flowchart similar to that of Figure 3 and shows routine procedures to be prosecuted in operation of the system illustrated in Figure 4; Figure 8 is an explanatory view of a universe of discourse and membership map prepared in the procedures in the flowchart shown in Figure 6 and stored in the control unit shown in Figure 5; Figure 9 is an explanatory graph showing the relationship between the universe of discourse the fuzzy labels used in the membership map; Figure 10 is an explanatory view of a membership map for evaluation indices prepared in the flowchart of Figure 6; Figure 11 is an explanatory graph of the evaluation indices of Figure 10; Figure 12 shows fuzzy production rules prepared in the procedures in the flowchart of Figure 6; Figure 13 is a first prediction table prepared in the procedures in the flowchart of Figure 6; Figure 14 is a second prediction table prepared in the procedures in the flowchart of Figure 6; Figure 15 is a third prediction table prepared in the procedures in the flowchart of Figure 6; Figure 16 is an explanatory graph of fuzzy labels used in the prediction tables shown in Figures 13 to 15; Figure 17 is a flowchart showing a subroutine for autocruise control in the flowchart of Figure 7; Figure 18(a) to Figure 18(g) are graphs showing examples of prediction of the following condition according to the procedures in the flowchart of Figure 17; Figure 19 is a table of the results of an example obtained by the determination according to the procedures in the flowchart of Figure 17; Figure 20 is a table used in the flowchart of Figure 17 for converting the control value of a selected rule into a real amount of throttle opening; Figure 21 is a graph showing the relationship between fuzzy labels and the universe of discourse used in the table shown in Figure 20; Figure 22 is a flowchart similar to that of Figure 6 but shows a second embodiment of the invention illustrating preparatory procedures for designing an autocruise control system similar to that shown in Figure 4; Figure 23 is a flowchart similar to that shown in Figure 17 and shows a subroutine for autocruise control according to the second embodiment of the invention; Figure 24 shows a second membership map prepared in the procedures in the flowchart of Figure 22; Figure 25 is a graph illustrating the relationship between the fuzzy labels and universe of discourse used in the map shown in Figure 24; Figure 26 is a flowchart showing a subroutine for the second fuzzy reasoning referred in the flowchart of Figure 23; Figure 27 is a rule table used in the flowchart of Figure 26; Figure 28 is an explanatory view showing fuzzy relational composition carried out according to the second fuzzy reasoning referred in the flowchart of Figure 26; Figure 29 is a computation table used in the calculation according to the flowchart of Figure 26; Figure 30 is a conversion table for converting the result of the second fuzzy reasoning into a real number according to the flowchart of in Figure 26. An explanation will first be given regarding the premises on which the invention is based. Usually states or conditions are represented in terms of physical values. However, it is also possible to represent states mathematically in terms of groups or sets, as is illustrated in Figure 1. For example, a given phenomenon may be perceived as divided into the three states or subsets of small, medium and large. In this case, if one subset is indisputably satisfied, its degree of satisfaction can be defined as "1.0" whereas if it indisputable fails to be satisfied, its degree of satisfaction can be defined as "0". It is also possible to define intermediate degrees of satisfaction as "0.5, 0.7 ..." Thus, as shown in the figure, a specific state a can, for instance, be said to have a degree of satisfaction in the "medium" subset of 0.5 but a degree of satisfaction in the "small" subset of only 0.1. This type of thinking falls within the scope of Fuzzy Logic and can be applied not only to objective physical phenomena but also to subjective non-physical phenomena such as feelings and impressions of a human being. Thus when applied to a system for vehicle operation, for example, Fuzzy Logic is not only capable of handling conventional physical quantities but also makes it possible, through psychological and statistical analysis, to introduce as factors indicating operating conditions such non-physical, subjective feelings and impressions as "drivability" or "steering response," and thus opens the way to the use of such impressions and feelings in the control of vehicle operation. The present invention relates to a vehicle control system which utilizes such a Fuzzy Logic or Approximate Reasoning. Before going into structural details of a system embodying the present invention, there follows a short discussion with reference to the flowcharts of Figures 2 and 3 for further elucidating a vehicle control system according to the invention. Figure 2 shows a preparatory routine carried out in designing a control system for vehicle operation. The preparatory routine begins with step S10 in which an object in the vehicle operation to be controlled such for example as an autocruise control or an automatic transmission control is determined and a system therefor is designed utilizing the Fuzzy Logic therein. Then in step S12 parameters required for detecting the operating condition of the vehicle are selected in accordance with the purpose of the control object to be controlled and a scale divided into a prescribed number of subdivisions in accordance with the operating condition of the vehicle is established for each selected parameter. While these parameters are generally physical quantities, it is also possible to include human judgments, feelings and other non-physical factors after these have been quantified using appropriate techniques. In the case of a physical value, there is used either the actually measured value or a computed value obtained, for example, by second-order differentiation of a measured value. In the following step S14, evaluation indices are selected and defined. The evaluation indices are determined based on the aforesaid parameters and, as will be explained later, are used as indices for evaluation of fuzzy production rules. Next, the fuzzy production rules are created in step S16. The rules are linguistically expressed and one feature of the rules used here is that they are of a predictive nature, looking ahead to the degree of satisfaction of the aforesaid evaluation indices which would be obtained should the output commands prescribed thereby be effected. In the following step S18, a prediction table is defined. The prediction table indicates changes in operating condition that would occur should the aforesaid output commands be effected and are expressed in terms of quantitative changes in the aforesaid parameters. The prediction table is determined in advance by experiment. The preparatory routine is completed by carrying out these steps S10 to S18. Figure 3 is a flowchart showing operation of the control system designed in accordance with the flowchart of Figure 2. First, the current operating condition is ascertained in step S100. This is done using the aforesaid parameters. Next, in step S102, the subdivision of the scale established in step S12 corresponding to the detected value of each parameter is determined. The procedure then advances to step S104 where the vehicle operation predicted to occur should the output commands prescribed in the aforesaid rules be effected is determined. This is done by retrieving predicted values from the table created in step S18. Following this, the rules are evaluated for selection of the most appropriate one in step S106. This is done by judging the extent to which the retrieved predicted values meet the degrees of satisfaction required by the respective evaluation indices. Here it should be noted that when a plurality of evaluation indices apply to a single rule, the evaluation is made based on the smallest evaluation value. It should also be noted that when there are a plurality of rules, one of them is selected, this being done, for example, by selecting as the one with the highest degree of satisfaction, the one whose evaluation value is greatest. The procedure then moves to step S108 where a control value is determined according to the output command included in the rule selected in the preceding step. The control value set in step S108 is then output to an actuator in the aforesaid system in step S110. Now, a vehicle control system according to the invention will be explained taking an autocruise control system as an example. Figure 4 is an overall schematic view of the system. In the figure, the reference numeral 10 denotes an engine which is supplied with fuel by a fuel injection device 12 located downstream of a throttle valve 14 provided in an intake air passage 16 which has an air cleaner 18 at its upstream end. The throttle value 14 is electrically, not mechanically, linked with an accelerator pedal 20 located on the floor of the vehicle at the operator's seat. The throttle valve is on the one hand connected to a pulse motor 22 by which it is driven to open and close. The accelerator pedal 20 has an accelerator sensor 24 for detecting the depressed degree of the pedal operated by the driver and for sending a signal in response to the detected value to a control unit 26. Further, a crankshaft angle sensor, not shown, is provided in the vicinity of a rotating member of the engine 10 and a pressure sensor, not shown, is provided downstream of the throttle valve 14 in the intake air passage 16. These detect the engine crankshaft angle and the absolute pressure in the intake air passage and send signals representing the detected values to the control unit 26. The stage following the engine 10 is a transmission 30 and a vehicle speed sensor, not shown, located thereat detects the vehicle speed and sends a vehicle speed signal to the control unit 26. On the steering wheel, not shown, there is further provided a main switch for issuing commands to start and stop the autocruise control, a set switch for issuing commands for setting a desired vehicle speed and for deceleration, and a resume switch for issuing commands for resuming autocruise control and for acceleration. The ON/OFF signals from these switches are similarly sent to the control unit 26. Further, a brake pedal 32 disposed at the side of the accelerator pedal 20 is provided with a brake switch 34 which sends a signal to the control unit 26 when the brake pedal 32 is depressed. Finally, a throttle sensor 36 is provided for detecting the opening degree of the throttle valve 14 and sends a signal to the unit 26. Figure 5 shows the detailed structure of the control unit 26. As is illustrated, the output signals of the accelerator sensor etc. are first level-converted through a level converting circuit 40 in the unit and then sent to a microcomputer 42. The microcomputer 42 is provided with an A/D converter 42a, an input terminal board 42b, a CPU 42c, a Rom 42d, a RAM 42e and an output terminal board 42f and the output from the circuit 40 is A/D converted through the converter 42a and is temporarily stored in the RAM 42e. Similarly, the output signals of the sensors including the crankshaft angle sensor are wave shaped through a wave shaping circuit 44 and forwarded to the microcomputer 42 through the input terminal board 42b. The microcomputer calculates a control value as described in detail at a later stage and issues a command through the output terminal board 42f to the pulse motor control circuit 48 in order to actuate the pulse motor 22 which in turn drives the throttle valve 14 in the required direction. The operation of the the vehicle control system according to this invention will now be explained with reference to Figure 6 and thereafter. Figure 6 is similar to Figure 2 and shows a preparatory routine in designing the autocruise control system shown in Figure 4. In figure 6, the procedure begins with step S200 in which parameters indicating the operating condition are selected. As the parameters in this embodiment, there are selected the vehicle speed difference VDIF between the set vehicle speed VSET and the actual vehicle speed V (km/h --speed in kilometers per hour), vehicle acceleration α (km/h/s -- change per second in vehicle speed) and change in vehicle acceleration Δα obtained by second-order differentiation of the vehicle speed (km/h/s/s --change in vehicle acceleration per second). The procedure then advances to step S202 in which there is established a scale divided into a prescribed number of subdivisions in accordance with the operating condition of the vehicle. These subdivisions are shown in Figure 8, from which it will be noted that the parameters VDIF, α and Δα are divided into 15 subdivisions assigned values centered on zero and ranging from +7 to -7 (this range of values being hereinafter referred to as the "universe of discourse u ). It should be noted that there is continuity between the subdivisions under the universe of discourse so that intermediate values can be computed by interpolation. As shown in the figure, the vehicle speed difference VDIF is correlated with the subdivisions of the universe of discourse over values ranging from not more than -7 km/h to not less than +7 km/h, the acceleration α is correlated with the same over values ranging from not more than -1.4 km/hr/s to not less than +1.4 km/h/s, and the change in acceleration Δα is correlated with the same over values ranging from not more than -2.8 km/h/s/s to not less than +2.8 km/h/s/s. Here a negative value of the vehicle speed difference VDIF indicates that the actual vehicle speed is lower than the set speed, while negative values of the acceleration α indicate deceleration. The symbols in the column on the far left of the same figure are what are called "fuzzy labels FL" in Fuzzy Logic. As will be noted, seven labels are assigned, namely, NB, NM, NS, ZO, PS, PM and PB, and the cells at the intersections between these fuzzy labels and the universe of discourse columns are assigned values between 0 and 1.0 (which will hereinafter be called "membership values µU) The table of Figure 8, hereinafter called a "membership map", is stored in the microcomputer of the control unit 26. The fuzzy labels are defined as follows: NB means Negative Big, NM means Negative Medium, NS means Negative Small, ZO means Zero, PS means Positive Small, PM means Positive Medium and PB means Positive Big. Figure 9 shows the relationship between the fuzzy labels and the universe of discourse in the map in the form of a graph. The procedure then advances to step S204 where vehicle performance evaluation indices E are selected, correlated with the subdivisions of the universe of discourse, tabulated in the form of a membership map, and stored in the ROM. The resulting membership map is shown in Figure 10. Five evaluation indices are selected in the present embodiment of the invention: Speed Conformity index E VA , Freshness index E FR , Comfort index E CF , Fuel Efficiency index E FE , and Safety index E SF . As can be seen in the drawings, like the membership map of Figure 8, the membership map of Figure 10 also has 15 subdivisions within the universe of discourse and membership values µVA, µFR, µCF, µFE and µSF are assigned to the cells at the intersections between the evaluation indices and the respective columns. Each evaluation index indicates the quality of an aspect of the vehicle performance at different states of the parameter concerned. More specifically, the Speed Conformity index E VA indicates the quality of the speed conformity as a value from 0 to 1.0 depending on the state of the vehicle speed difference VDIF. Similarly, the Freshness index E FR and the Comfort index E CF indicate the quality of the freshness and the comfort depending on the state of the change in acceleration Δα, the Fuel Efficiency index E FE indicates the quality of the fuel efficiency depending on the state of the acceleration α, and the Safety index E SF indicates the quality of the safety depending on the state of the speed difference VDIF. These relationships will be explained briefly with reference to Figure 11 which shows them in graphic form. When the value of the speed difference VDIF is zero, this means that there is no difference between the set speed and the actual speed. Thus the membership value µVA of the speed conformity index E VA is defined as the maximum value of 1.0 in the vicinity u = 0 in the universe of discourse. It being known from experience that the passengers are given a fresh feeling when the change in acceleration is a small positive value, the membership value µFR of the Freshness index E FR is defined by a curve sloping upward toward the right side of the universe. It having been confirmed from experience that the passengers are given a comfortable feeling when the acceleration α is constant, the membership value µCF of the Comfort index E CF is defined as being maximum in the vicinity of u = 0, where there is no change in acceleration. Since fuel economy is known to increase with increasing deceleration (negative acceleration), the membership value µFE of the Fuel Efficiency index E FE is defined by a curve rising to the left. Since it has been confirmed by experience that safety increases with decreasing difference between set speed and the actual speed, because there is less likelihood of a change in vehicle speed when this difference is small, the membership value µSF of the Safety index E SF is defined as being maximum in the vicinity of u = 0. While the membership values µE have been described as being determined experimentally and from experience, they can, needless to say, be alternatively defined by other manners. Moreover, it is alternatively possible to compute the lapse of time up to the inflection point at which the acceleration turns negative, and to use this lapsed time as the parameter on which the membership value of the Comfort index E CF is defined. The procedure then moves to step S206 in which the fuzzy production rules Ri are created. Five rules Ri1 - Ri5 are established as shown in Figure 12. The rules are expressed linguistically (in words) and include a predictive aspect. For example, control rule Ri1 prescribes: "Close the throttle valve slightly if the Speed Conformity and the Comfort will be satisfied after this has been done." The other rules have similar predictive aspects. The rules prescribed an output command Ci, which in this embodiment is indicated by a fuzzy label FLCi. Further, while each control rule requires evaluation of one or more performance aspects, the matter of which performance aspects to include can be decided as desired based on the cause-and-effect relationship judged to exist with the object of the control. One criterion that can be considered for use in this selection is that if a given performance aspect need not be considered under the operating condition concerned, then it should not be included in the rule concerned. This is because requiring consideration to be given to a performance aspect that need not be considered may prevent consideration of other important performance aspects and possibly lead to the rule itself not being selected. The procedure then advances to step S208 where a prediction table is defined. For this, the changes that would occur in the operating condition should the rules be applied one by one and their output commands implemented are defined in terms of the quantitative changes that would occur in the aforesaid parameters VDIF, α and Δα which will hereinafter be referred to as predicted values P (VDIFp, αp and Δαp), and the results are assigned the fuzzy label FL p . Figures 13, 14 and 15 show tables illustrating the predicted values defined for the respective parameters in this way. The prediction tables are compiled in advance on the basis of experiments and are stored in the aforesaid ROM. Figure 16 shows the fuzzy labels FL p used for these predicted values in the form of a graph. Next, the mode operation of the autocruise control system shown in Figure 4 will be explained hereinafter with reference to Figure 7. In the first step S300 it is judged whether or not the aforesaid main switch is ON and if the judgment is NO, the autocruise control is not conducted (step S302). When it is determined that the main switch is ON, the procedure moves to step 304 where the amount of throttle valve opening ϑTH is determined from the output of the throttle sensor 36 or accumulated values fed to the pulse motor 22, and the vehicle speed V, vehicle acceleration α and change in acceleration Δα are computed on the basis of the output of the vehicle speed sensor. More specifically, the vehicle speed V is computed on the basis of the average output value of the vehicle speed sensor over a prescribed time period, the acceleration α is computed by dividing the vehicle speed change by time in seconds and the change in acceleration Δα is computed by dividing the acceleration α change by time in seconds. Next, in the step S306, it is determined whether or not the vehicle speed V computed in the preceding step is greater than a prescribed vehicle speed Vref, which may, for example, be 20 km/h, and when it is, the procedure moves to step S308 where it is determined whether or not the brake switch 34 is ON. In these steps, when it is found that the vehicle speed V is lower than the prescribed vehicle speed Vref or when it is found that the brake switch 34 is ON, autocruise control is not carried out (step S302). In the following step S310 it is determined whether or not autocruise control is currently being conducted, and when it is not, the procedure moves to step S312 where it is determined whether or not a set flag is ON. If the set flag is ON, the procedure goes to step S314 where the vehicle speed V at that instant is read as the set vehicle speed VSET and in the ensuing step S316 the difference between the set vehicle speed VSET and the vehicle speed V detected in step S304 is computed as vehicle speed difference VDIF. Then in step S318 the amount of throttle valve opening ϑTH is set to a target value. What this amounts to is an initializing operation carried out prior to autocruise control for gradually opening the throttle valve to an amount corresponding to the set vehicle speed VSET. This is necessary because when the operator pushes the set switch and immediately takes his foot off the accelerator pedal, the throttle valve may open rapidly depending on the condition of the road on which the vehicle is traveling. The procedure then advances to step S320 where the shift to autocruise control is made. This will be explained with reference to the flowchart of Figure 17 which shows a subroutine for autocruise control. However, regarding the main routine of Figure 7, it should be noted that when it is determined in step S310 that autocruise control is currently in progress, the procedure moves to step S322 where it is determined whether or not the set switch is ON, and if it is, deceleration control is carried out (step S324). When it is determined in step S322 that the set switch is not ON, the procedure moves to step S326 where it is determined whether or not the resume switch is ON, and if it is, acceleration control is conducted (step S328), and if it is not, the control mode is shifted over to autocruise control. Further, when it is determined in step S312 that the set flag is not ON, autocruise control is not conducted (step S302). As the gist of the present invention resides in the autocruise control conducted mainly in the step S320, explanation of the main flowchart will be left at this. Figure 17 is a flowchart showing the AC subroutine in step S320 in the aforesaid flowchart. The subroutine starts with step S400 in which the table of Figure 8 is used for determining the position or value within the universe of the parameters computed in steps S304 and S316 in the Figure 7 flowchart and indicating the current operating condition, namely, speed difference VDIF, acceleration α and change in acceleration Δα. Then in steps S402, S404 and S406, the fuzzy labels FLVDIF, FLα and FLΔα corresponding to the respective parameters are determined. Taking a concrete example, suppose that under the current operating condition vehicle speed difference VDIF = 0 km/h, acceleration α = 0 km/h/s and change in acceleration Δα = 0 km/h/s/s. In this case, the value of all the parameters within the universe would be u = 0. Checking the u = 0 column, it is found that the membership values corresponding to the fuzzy labels are 0, 0, 0, 1.0, 0, 0, 0. Thus the membership value of the fuzzy label ZO is 1.0, while that for all the other fuzzy labels is zero. Thus the only fuzzy label concerned is ZO. Therefore the results of the determination are: Parameter Fuzzy label Speed difference VDIF ZO Acceleration α ZO Change in acceleration Δα ZO The procedure then advances to step S408 where, referring to the prediction tables of Figures 13 to 15, the fuzzy labels FL p indicating the predicted values P (VDIFp, αp and Δαp) of the parameters during the following operating condition (at a later time n + 1) are computed from the aforesaid determined fuzzy labels FLVDIF, FLα and FLΔα and the fuzzy labels FLCi of the output commands Ci of the rules Ri1 to Ri5. By "following operating condition (at time n + 1)" is meant the operating condition at a point in time which is later than the present point in time, and if the procedure of the flowchart of Figure 7 is started once every prescribed time interval or once every prescribed advance in crankshaft angle, this expression can be defined to mean "at the start of the next cycle of the flowchart procedures." Alternatively, it may of course be defined as referring to the start of the cycle after next or to the start of some even later cycle. In the aforesaid example, the predicted values become as follows: In the following step S410, the evaluation values µE of the respective rules are determined from the membership values (µVA, µFR, µCF, µFE, µSF) of the evaluation indices and the membership values mp of the fuzzy labels FL p of the predicted values. Thus in the case of the aforesaid example, since the evaluation indices of the first rule are the Speed Conformity index E VA ( parameter VDIF) and the Comfort index E CF (parameter Δα) and the output command fuzzy label FLCi of control rule 1 is NS, the evaluation value µE of the Speed Conformity index E VA is 0.8, as shown in Figure 18(a), namely the maximum value at the portion where the graph for the Speed Conformity index E VA and the fuzzy label NS of the speed difference VDIF overlap. Similarly, since the fuzzy label of the acceleration change Δα is also NS, the evaluation value µE of the Comfort index E CF is 0.7. Further, since the only evaluation index of rule 2 is the Speed Conformity index E VA (parameter VDIF) and the fuzzy label of VDIF is ZO, the evaluation value µE is 1.0, as can be seen from Figure 18(c). Since the evaluation indices of rule 3 are the Speed Conformity index E VA , the Comfort index E CF , the Fuel Efficiency index E FE and the Safety index E SF , the evaluation values µE are, as shown respectively in Figures 18(d) to 18(g), 0.82, 0.75, 0.15 and 1.0. Similarly, in the case of rule 4, the evaluation values of the Speed Conformity index E VA and the Comfort index E CF are 0.25 and 0.2, respectively, and in the case of rule 5, the evaluation values of the Speed Conformity index E VA , the Freshness index E FR and the Safety index E SF are 0.25, 1.0 and 0.4, respectively. The foregoing is summarized in Figure 19. For conducting these computations, space is set aside in the RAM of the microcomputer 42 for a table like that of this figure. After the completion of each computation, resetting is carried out in an appropriate step (not shown) established for this purpose. If resetting to zero is done, however, at the time of the computation of the minimum values (which will be explained below) it becomes impossible to distinguish between an actual zero and an unused zero. Thus FF (overflow) resetting is used and the value which overflows is not considered in the determination of the minimum value. Next the final evaluation values µRi of the rules are found. In this case, since the evaluation values (membership values µE) indicate the degree of satisfaction of the evaluation indices, there is taken as the evaluation value µRi in each case the minimum value among those for the respective evaluation indices, on the presumption that such minimum value "will satisfy all the evaluation indices at least within the range thereof." Thus the final evaluation values µRi are as follows: The procedure then advances to step S412 where one of the five rules is selected. At this time, the rule which gave the largest final evaluation value µRi in the preceding step is selected because a larger final evaluation value means a greater degree of satisfaction. Thus in the example, since rule 2 gives the largest value (1.0), this rule is selected as the applied rule Rout. The second control rule is: "Do not change the throttle valve opening if the Speed Conformity will be satisfied without doing so." Therefore, the output command U(Rout) of this selected rule becomes the control value Uout. A conversion table for Uout is shown in Figure 20, wherein the upper row shows the control value Uout classified in a universe of discourse that is divided into 15 subdivisions, and the lower row defines corresponding amounts of opening degree ϑTH to which the throttle valve is to be controlled. In the case of the example, since the selected evaluation value µRi indicates a degree of satisfaction of 1.0 and since, in the case of the ZO fuzzy set, the Uout giving a value of 1.0 is u = 0 as shown in Figure 21, the amount of throttle valve opening ϑTH is 0 degrees. In the case where a plurality of output values are obtained at the time of conversion, the smallest one is selected from the viewpoint of precaution. Another example will be explained. Assuming that under the current operating conditions speed difference VDIF = 3 km/h, acceleration α = 0.6 km/h/s, and change in acceleration Δα = 1.2 km/h/s/s, the respective fuzzy labels will be found to be: Moreover, the predicted values upon implementation of the respective rules will be as shown below. In this case, since the current operating condition involves a plurality of fuzzy labels, there will also be a plurality of fuzzy labels for the predictions. The predicted values for the respective control rules are thus While as in the preceding example the smallest values are taken as degrees of satisfaction for the respective rules, in this second example there are a plurality of evaluation values for each of the evaluation indices. Therefore, the largest value is first selected from among those for each of the evaluation indices in the sense that the prediction is more suited and the smallest values are selected thereafter. As a result, the smallest values will be: Thus, rule 1 is selected, which prescribes: "Close the throttle valve slightly if the Speed Conformity and the Comfort will be satisfied after this has been done." In converting into an output value, NS becomes 1.0 when Uout = -2 as is shown in Figures 20 and 21 so that the throttle opening degree is determined to be -0.5 degree. Thus the throttle valve is closed by 0.5 degree. Returning to the flowchart of Figure 7, in step S330 the aforesaid control value is output to the pulse motor control circuit 48 to drive the pulse motor 22 for opening/closing the throttle valve 14 by the required amount. As explained in the foregoing, by the use of Fuzzy Logic, the invention opens the way to the incorporation of human feelings and impressions in the control. It also opens the way to enabling the predictive judgments of an experienced driver to be defined in the form of prediction tables and incorporated in the control. As a result it becomes possible for finer control to be carried out with high precision by means of simple rules, thus improving the driver's feeling that the vehicle is an extension of himself, while also opening the way to the realization of automatic control which simulates the safe, economical and comfort-producing driving techniques of a skilled driver effected on a non-automated vehicle. A second embodiment of the invention is illustrated in Figures 22 to 30, which is also embodied as the autocruise control system shown in Figure 4. The preparatory routine of the second embodiment is shown in Figure 22. The steps S500 to S506 in the flowchart of Figure 22 are nearly identical to those of steps s200 to s206 of Figure 6, and differ therefrom only in the procedure at step S508. More specifically, as will be explained in greater detail later, in this second embodiment the procedure at step S508 determines the final control value by subjecting the output command of the selected rule to a second fuzzy reasoning or inference, and for the purpose of the step S508 there are defined and stored in the ROM a second parameter membership map, a second rule table and a second output conversion table, which will be explained later. Thus the procedure of the second embodiment is identical to that of the first up to the selection of the rule, but differs therefrom in how the output value is determined from the selected rule. This point will be explained with reference to Figure 23, which is similar to that of Figure 17 and shows another AC subroutine flowchart for autocruise control shown in Figure 7. After the rule providing the highest degree of satisfaction has been selected in steps S600 to S612, the parameters determined in steps S304 and S316 in Figure 7 flowchart as indicating the current operating condition are in step S614 once again determined with regard to their positions within the universe of discourse. The aforesaid second membership map used for this conversion is shown in Figure 24. In this case since two types of parameters are sufficient, there are used the vehicle speed difference VDIF and the acceleration α. While the membership map of Figure 24 is generally similar to that of Figure 8, it differs in that the parameters are separately classified for each rule output command (fuzzy label FLCi). Figure 25 represents the relationship between the fuzzy labels and the universe of discourse in graphic form. Thus in the conversion to values within the universe carried out in step S614, the output command fuzzy labels selected in the preceding step, i.e. the subdivisions concerned of the universe are selected from the current operating condition. For instance, in the first example discussed earlier in which VDIF = 0 km/h and α = 0 km/h/s, since the selected rule was rule 2 whose command was ZO, the subdivision of the universe where VDIF = 0 and α = 0 in the ZO column at the upper portion in Figure 24 is just above u =0 for both VDIF and α so that the subdivision selected would be u = 0. Next in step S616, the second fuzzy reasoning is carried out based on the results of the selection. This will be explained with reference to the flowchart of Figure 26. What will be referred to here as the "second fuzzy reasoning" is similar to the fuzzy reasoning technique employed in the first embodiment and the preceding steps S600 to S612 in this embodiment but differs in that it does not include predictive nature. For ease of understanding, the second reasoning will first be explained with respect to the earlier examples, in both of which the selected subdivision of the universe was u = 0. As can be seen in Figure 25, if a line is drawn vertically upward from the position of this zero, it will intersect only with the fuzzy label ZO. Next, with reference to the rule table of Figure 27, the combination of fuzzy production rules prepared for the second reasoning involving the fuzzy label ZO of difference VDIF and the fuzzy label ZO of acceleration α is as shown below. When a rule of this table is expressed linguistically, it generally becomes such as "IF parameter a = x and parameter b = y , THEN control value = z . The control value is retrieved using the values of parameters VDIF and α as address data. This means "If vehicle speed difference VDIF is zero and acceleration α is zero, then do not change the amount of throttle valve opening ϑTH." Then, as shown in Figure 28, all fuzzy labels involved in the IF and THEN portions are extracted. In the case under discussion, therefore, three fuzzy labels are extracted: VDIF = ZO, α = ZO and ϑTH = Z0. The triangular representations of the two fuzzy labels of the IF portion are compared, a minimum value is selected and the minimum value is used for truncating the triangular representation of the THEN portion. In the example, in Figure 28 if in the fuzzy label ZO of the vehicle speed difference VDIF a line is drawn vertically upward from the position 1.0 on the universe, it will intersect the triangular representation at the membership value µ of 1.0. The membership value µ for the acceleration α is also 1.0. These values are compared and the smaller i.e. 1.0, is then selected and used as the height at which to laterally truncate the triangular representation of the fuzzy label ZO of the THEN portion. In the example, since the truncation is carried out at the height of 1.0, the triangular representation of the THEN portion is not actually cut. Then the remaining base portion, hatched portion, obtained in this way from the triangular representation of ZO is superposed on the universe u . Normally about 4 triangular representations will be involved but since in the example there is only one triangular representation in the THEN portion, the result of the composite superposing is identical with the original triangular representation. The desired control value is within the so-obtained composite hatched portion. In order to obtain a specific numerical value, the center of gravity G of the composite hatched portion is determined and a line is drawn vertically downward therefrom. The value at the point where this vertical line intersects the universe u is taken as the control value, which in this example is zero. This second fuzzy reasoning will now be explained with reference to the flowchart of Figure 26. First, a computation table is initialized in step S700. This table is shown in Figure 29 and is stored in the microcomputer 42. While computation values µOUTij, similar to the values referred to in the earlier example, are successively written into the central part of this table in a later step of the procedure, all that is done in this step is to secure space for computation of this table. The initial values are zero. The procedure then advances to step S702 in which the membership values for all vehicle speed difference VDIF fuzzy labels from µNB(VDIF) = k1 to µPB(VDIF) =k7 are retrieved. Then similarly, in the following step S704, the membership values for all acceleration α fuzzy labels from µNB(α) = l1 to µPB(α) = 17 are retrieved. In the foregoing example, this amounts to reading in the values 0, 0, 1.0, 0, 0, 0 in the column under the u = 0 subdivision of the universe, which is the subdivision in which VDIF and α fall, and then writing these values into the aforesaid computation table. As was mentioned earlier, when no corresponding column exists, the value is determined by interpolation from the values of the two nearest columns. In the succeeding step S706, the retrieved values, k nm = MIN (k n , l m ) (fuzzy label numbers n, m = 1, 2 ...7) are compared and the minimum value is selected. This corresponds to the comparison of the two ZO values 1.0 and 1.0 of the IF portion in the earlier example. The procedure then advances to step S708 in which it is judged whether or not the result of the comparison is zero, and if it is not, the procedure moves to step S710 in which the fuzzy label numbers n, m are used to retrieve the fuzzy label p nm of the THEN portion from the rule table of Figure 27. This corresponds to the operation in the earlier example of using VDIF and α for selecting the intersecting THEN portion fuzzy label ZO. The other rules in the rule table of Figure 27 are listed below. Where a cell in the table is left empty, this indicates that no rule exists, and in actual practice a zero is inserted in the cell. 1. NB ZO PB (If the actual vehicle speed is much smaller than the set vehicle speed and the acceleration is zero, then open the throttle valve by a great amount.) 2. NB PS PM (If the actual vehicle speed is much smaller than the set vehicle speed and the vehicle is accelerating slightly, then open the throttle valve by a moderate amount.) 3. NM ZO PM (If the actual vehicle speed is moderately smaller than the set vehicle speed and the vehicle acceleration is zero, then open the throttle valve by a moderate amount.) The procedure then advances to step S712 in which it is judged whether or not the fuzzy label p nm of the THEN portion is "0," that is, whether or not there is a THEN portion. If it is not zero, the procedure advances to step S714 in which µout for p nm is computed. This corresponds to the operation previously explained with reference to Figure 28 of truncating the triangular representation of the THEN portion based on the smallest value among the triangular representations in the IF portion. This operation is repeated for every rule (step S716). In the example, since the selected subdivision of the universe was u = 0, the IF portion involved only one rule. Ordinarily, however, there will be about four triangular representations involved and the foregoing procedures have to be carried out for all. Incidentally, in step S708, since the rule has no IF portion in the case where the minimum value of k nm is zero, in such a case the computation is discontinued and the procedure jumps directly to step S716. In step S712, on the other hand, the control rule has no THEN portion when p nm is zero and so in this case too the procedure jumps directly to step S716. The procedure then advances to step S718 in which the computed values are summarized to calculate composite value µH(u). This corresponds to the operation previously explained with reference to Figure 28 of superposing on the universe. Then in step S720 it is judged whether or not the composite value is zero, and if it is not, the center of gravity G is derived in step S722 by weighted averaging or the like, whereafter the corresponding value on the universe is determined. When the composite value is found to be zero in step 720, this means that there is no THEN portion and the computation for finding the center of gravity is not carried out. Returning to the flowchart of Figure 23, following the fuzzy reasoning of step S616, the procedure moves to step S618 where the value on the universe is converted into a real number. This is done using the conversion table shown in Figure 30. In this figure, the top row is divided into subdivisions of the universe, while the lower rows, one for each fuzzy label, are subdivided into amounts of throttle valve opening ϑTH. At the time of conversion, therefore, retrieval is conducted once again using the fuzzy label (output command) of the rule Ri selected in step S612. In the case of the example, since the fuzzy label of the output command was ZO, the value at u = 0 in the ZO column is selected. Thus the value selected is 0. A separate conversion table is established for each rule. The aforesaid value is output to the pulse motor control circuit 48 according to procedures in a flowchart, not shown, similar to that of Figure 7, whereby, as in the first embodiment, the pulse motor 22 is driven to open/close the throttle valve 14 by the required amount. While in the embodiment just described fuzzy reasoning is used for determination of the output command following step S612 in Figure 23 flowchart, this determination need not necessarily be conducted using the second fuzzy reasoning but can instead be conducted using the conventional proportional-plus-integral-plus-differential control technique.

Claims: A vehicle control system, including: first means (40,44) for detecting operating parameters of the vehicle including the vehicle speed; second means (42) for carrying out fuzzy reasoning to determine a control value (U OUT ) by retrieving a plurality of fuzzy production rules (Ri) prepared on the basis of human expertise, said rules being made up of a premise including at least one evaluation index (E) assigned with a fuzzy membership function and based on the detected vehicle operating parameters and a conclusion in the form of an output command (U(R OUT )), said second means carrying out the fuzzy reasoning by evaluating the satisfaction degree of the premise through the calculation of the membership value of the index or indices (E) based on the predicted change (P) in the vehicle operating parameters which would occur if the conclusion is applied and by selecting one of the rules with the maximum degree of satisfaction to determine the control value (U OUT ); and third means (48) for driving an actuator (22) in accordance with the determined control value; characterized in that: said vehicle control system is an automobile cruise control system for keeping the travelling speed of the vehicle at a predetermined speed; said output command (U(R OUT )) is expressed as a term of a fuzzy membership function; and said second means is adapted to convert the output command determined into a real number control value (U OUT ) in advance of being provided to said third means. A system according to claim 1, wherein said detected vehicle operating parameters are assigned with a fuzzy membership function; said second means is adapted to predict the vehicle operating parameter change based on the fuzzy membership function of the vehicle operating parameters; and said second means evaluates the satisfaction degree of the premise by calculating the membership value of the evaluation index or indexes with the assigned membership function of the vehicle operating parameters. A system according to any preceding claim, wherein said second means is adapted to carry out a second fuzzy reasoning on the determined output command (R OUT ) to determine a final control value (U OUT ). A system according to claim 3, wherein said second means carries out the second fuzzy reasoning with the detected operating parameters and a fuzzy membership function newly assigned thereto. A system according to any preceding claim, wherein said vehicle operating parameters are vehicle speed and its n-th differential.

Inventors: ARAI, YASUHISA C/O KABUSHIKI KAISHA HONDA, HOSAKA, TAKEFUMI C/O KABUSHIKI KAISHA HONDA, MATSUI, HIROKI C/O KABUSHIKI KAISHA HONDA
Applicants: HONDA GIKEN KOGYO KABUSHIKI KAISHA
Application year: 1988
Main classification: G05B13
IPC codes: G05B13, B60K31