Control apparatus, control method and recording medium having recorded thereon control program

Provided is a control apparatus comprising a control unit configured to control a control target by a control model machine-learned so as to output an operation amount of the control target according to a state of equipment provided with the control target; a simulation unit configured to simulate, by using a simulation model, the state of the equipment in a case where the operation amount, which is output by the control model, is given to the control target; and a stop unit configured to stop control of the control target by the control model, based on a simulation result.

BACKGROUND

1. Technical Field

The present invention relates to a control apparatus, a control method and a recording medium having recorded thereon a control program.

2. Related Art

Patent Document 1 discloses ‘performs the machine learning of a compensation amount of a teaching position of a robot with respect to a disturbance produced in a motor that drives each joint of the robot, and compensates and controls the teaching position so as to reduce the disturbance when the robot moves to the teaching position, based on a result of the machine learning’.

CITATION LIST

Patent Document

SUMMARY

A first aspect of the present invention provides a control apparatus. The control apparatus may comprise a control unit configured to control a control target by a control model machine-learned so as to output an operation amount of the control target according to a state of equipment provided with the control target. The control apparatus may comprise a simulation unit configured to simulate, by using a simulation model, the state of the equipment in a case where the operation amount, which is output by the control model, is given to the control target. The control apparatus may comprise a stop unit configured to stop the control of the control target by the control model, based on a simulation result.

The stop unit may be configured to stop the control of the control target by the control model when the simulation result indicates occurrence of an abnormality in the equipment.

The control apparatus may further comprise an output unit configured to output the simulation result. The stop unit may be configured to stop the control of the control target by the control model when the stop unit acquires an instruction to stop the control in response to the output of the simulation result.

The control apparatus may further comprise a frequency adjustment unit configured to adjust a frequency of simulating the state of the equipment.

The frequency adjustment unit may be configured to reduce the frequency of simulation as an elapsed time since the control of the control target by the control model is started becomes longer.

The control apparatus may further comprise a switching unit configured to switch whether to input or shut off an output by the control model with respect to the control target. The stop unit may be configured to shut off the switching unit when stopping the control of the control target by the control model.

The switching unit may be configured by a physical switch.

The control apparatus may further comprise an instruction unit configured to instruct the control target to switch to control by another control means when stopping the control of the control target by the control model.

The simulation model may be a simple model capable of simulating the state of the equipment in a shorter time than a time spent on an actual operation of the equipment.

The control apparatus may further comprise a state data acquisition unit configured to acquire state data indicative of the state of the equipment. The control apparatus may further comprise an operation amount data acquisition unit configured to acquire operation amount data indicative of the operation amount. The control apparatus may further comprise a control model generation unit configured to generate the control model by machine learning by using the state data and the operation amount data.

The control model generation unit may be configured to generate the control model by performing reinforcement learning so that an operation amount whose reward value determined by a predetermined reward function is higher is output as a more recommended operation amount, in response to an input of the state data.

A second aspect of the present invention provides a control method. The control method may comprise controlling a control target by a control model machine-learned so as to output an operation amount of the control target according to a state of equipment provided with the control target. The control method may comprise simulating, by using a simulation model, the state of the equipment in a case where an operation amount, which is output by the control model, is given to the control target. The control method may comprise stopping the control of the control target by the control model, based on a simulation result.

A third aspect of the present invention provides a recording medium having recorded thereon a control program. The control program may be configured to be executed by a computer. The control program may be configured to cause the computer to function as a control unit configured to control a control target by a control model machine-learned so as to output an operation amount of the control target according to a state of equipment provided with the control target. The control program may be configured to cause the computer to function as a simulation unit configured to simulate, by using a simulation model, the state of the equipment in a case where the operation amount, which is output by the control model, is given to the control target. The control program may be configured to cause the computer to function as a stop unit configured to stop the control of the control target by the control model, based on a simulation result.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, the present invention will be described through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. In addition, not all combinations of features described in the embodiments are essential to the solution of the invention.

FIG.1shows an example of a block diagram of a control apparatus100according to the present embodiment, together with equipment10provided with a control target20. The control apparatus100according to the present embodiment is configured, during control (also referred to as artificial intelligence (AI) control) of the control target20by a learning model generated by machine learning, to simulate a state of the equipment10in a case where an output of the learning model is given to the control target20. The control apparatus100according to the present embodiment is configured to stop the AI control based on a simulation result.

The equipment10is a facility, an apparatus, or the like provided with the control target20. For example, the equipment10may be a plant or a complex apparatus in which a plurality of devices is combined. Examples of the plant may include a plant for managing and controlling well sites such as a gas field and an oil field and surroundings thereof, a plant for managing and controlling power generations such as hydroelectric, thermo electric, and nuclear power generations, a plant for managing and controlling environmental power generation such as solar power and wind power, a plant for managing and controlling water and sewerage, a dam and the like, and the like, in addition to industrial plants such as chemical and bio industrial plants.

The equipment10is provided with the control target20. In the present drawing, a case where the equipment10is provided with only one control target20is shown as an example, but the present invention is not limited thereto. The equipment10may be provided with a plurality of control targets20.

In addition, the equipment10may be provided with one or more sensors (not shown) configured to measure a variety of states (physical quantities) inside and outside the equipment10. Such sensors are configured to measure, for example, operation data, consumption data, external environment data, and the like.

Here, the operation data indicates an operation state as a result of controlling the control target20. For example, the operation data may indicate a measured value PV (Process Variable) measured for the control target20, and as an example, may indicate an output (control amount) of the control target20, or a variety of values that are changed by an output of the control target20.

The consumption data indicates an amount of consumption of at least one of energy or raw material in the equipment10. For example, the consumption data may indicate an amount of consumption of electric power or fuel (as an example, LPG: Liquefied Petroleum Gas), as energy consumption.

The external environment data indicates a physical quantity that can act as a disturbance with respect to control of the control target20. For example, the external environment data may indicate a temperature and a humidity of an outside air of the equipment10, a sunshine, a wind direction, an air volume, an amount of precipitation, and various physical quantities that change with control of other devices provided to the equipment10.

The control target20is a device, an apparatus or the like that is a target of control. For example, the control target20may be an actuator such as a valve, a pump, a heater, a fan, a motor and a switch that is configured to control at least one physical quantity such as a pressure, a temperature, a pH, a speed or a flow rate in a process of the equipment10, and is configured to input a given operation amount MV (Manipulated Variable) and to output a control amount.

The control apparatus100according to the present embodiment is configured, during control (AI control) of the control target20by a learning model generated by machine learning, to simulate a state of the equipment10in a case where an output of the learning model is given to the control target20. The control apparatus100according to the present embodiment is configured to stop the AI control based on a simulation result.

The control apparatus100may be a computer such as a PC (personal computer), a tablet-type computer, a smart phone, a workstation, a server computer or a general-purpose computer, or a computer system where a plurality of computers is connected. Such computer system is also a computer in a broad sense. In addition, the control apparatus100may also be implemented by one or more virtual computer environments that can be executed in the computer. Instead of this, the control apparatus100may also be a dedicated computer designed for AI control or dedicated hardware implemented by dedicated circuitry. Further, in a case where the control apparatus100can connect to the Internet, the control apparatus100may also be implemented by cloud computing.

The control apparatus100comprises a state data acquisition unit110, an operation amount data acquisition unit120, a control model generation unit130, a control model135, a control unit140, a simulation unit150, a simulation model155, and a stop unit160. Note that, these blocks are functional blocks that are each functionally divided, and are not necessarily required to be matched with actual device configurations. That is, one block shown in the present drawing is not necessarily required to be configured by one device. In addition, separate blocks shown in the present drawing are not necessarily required to be configured by separate devices.

The state data acquisition unit110is configured to acquire state data indicative of a state of the equipment10provided with the control target20. For example, the state data acquisition unit110is configured to acquire operation data, consumption data, external environment data and the like measured by sensors provided to the equipment10from the sensors via a network, as the state data. However, the present invention is not limited thereto. The state data acquisition unit110may also be configured to acquire such state data from an operator, or to acquire such state data from various memory devices and the like. The state data acquisition unit110is configured to supply the acquired state data to the control model generation unit130and the control model135.

The operation amount data acquisition unit120is configured to acquire operation amount data indicative of an operation amount of the control target20. For example, the operation amount data acquisition unit120is configured to acquire data, which indicates an operation amount MV (AI) that is output by the control model135when AI-controlling the control target20, from the control unit140, as the operation amount data. However, the present invention is not limited thereto. The operation amount data acquisition unit120may also be configured to acquire such operation amount data from an operator, or to acquire such operation amount data from various memory devices. The operation amount data acquisition unit120is configured to supply the acquired operation amount data to the control model generation unit130.

Note that, in the present drawing, a case where the operation amount data acquisition unit120acquires, as the operation amount data, data indicative of the operation amount MV (AI) output by the control model135is shown as an example. However, the present invention is not limited thereto. When the control apparatus100performs machine-learning of a model by using, as learning data, data under control of the control target20by a further controller (not shown), the operation amount data acquisition unit120may be configured to acquire, as the operation amount data, data indicative of an operation amount given to the control target20from the further controller. As an example, when the control of the control target20can switch between feedback control by an operation amount MV (FB: FeedBack) given from a further controller and AI control by an operation amount MV (AI) given from the control model135, the operation amount data acquisition unit120may also be configured to acquire data indicating the operation amount MV (FB) given to the control target20from the further controller, as the operation amount data. Note that, such FB control may be control using at least one of proportional control (P control), integral control (I control) or differential control (D control), for example, and may be, as an example, PID control. In addition, such further controller may also be integrally configured as a part of the control apparatus100according to the present embodiment, or may also be configured as a separate body independent of the control apparatus100.

The control model generation unit130is configured to generate a control model135, which outputs an operation amount corresponding to a state of the equipment10, by machine learning by using the state data and the operation amount data. For example, the control model generation unit130is configured to generate a control model135, which outputs the operation amount MV (AI) corresponding to a state of the equipment10, by reinforcement-learning, as learning data, the state data supplied from the state data acquisition unit110and the data indicative of the operation amount MV (AI) supplied from the operation amount data acquisition unit120. That is, the control model generation unit130is configured to generate the control model135by performing reinforcement learning so that an operation amount whose reward value determined by a predetermined reward function is higher is output as a more recommended operation amount, in response to an input of the state data. This will be described later in detail.

The control model135is a learning model generated as a result of the reinforcement learning by the control model generation unit130, and is configured to output the operation amount MV (AI) corresponding to the state of the equipment10. For example, the control model135is configured to input the state data supplied from the state data acquisition unit110and to output a recommended operation amount MV (AI) that is to be given to the control target20according to the state of the equipment10. Note that, in the present drawing, a case where the control model135is built in the control apparatus100is shown as an example. However, the present invention is not limited thereto. The control model135may also be stored in an apparatus (for example, on a cloud server) different from the control apparatus100. Similarly, the control model generation unit130may also be provided to an apparatus different from the control apparatus100.

The control unit140is configured to supply the operation amount MV (AI) output by the control model135to the control target20. Thereby, the control unit140is configured to control the control target20by the control model135machine-learned so as to output the operation amount of the control target20according to the state of the equipment10provided with the control target20. In addition, the control unit140is configured to supply the operation amount MV (AI) output by the control model135to the simulation unit150and the operation amount data acquisition unit120.

The simulation unit150is configured, by using the simulation model155, to simulate the state of the equipment10in a case where the operation amount MV (AI) output by the control model135is given to the control target20. Note that, as used herein, ‘simulating’ includes a case where the simulation unit150causes another apparatus (for example, a simulator (not shown)) to simulate a state of the equipment10and acquires the state of the equipment10simulated by another apparatus from another apparatus, in addition to a case where the simulation unit150plays a central role in simulating the state of the equipment10by itself. For example, the simulation unit150is configured to input, to the simulation model155, the operation amount MV (AI) output by the control model135, and to acquire a plurality of output values output by the simulation model155, as a simulation result. The simulation unit150is configured to supply the acquired simulation result to the stop unit160.

The simulation model155is a model (for example, a plant model) constructed so as to simulate a behavior of the equipment10. For example, the simulation model155is configured to simulate a behavior of the equipment10in a case where the operation amount MV (AI) is input and the operation amount MV (AI) is given to the control target20. Then, the simulation model155is configured to output a plurality of output values indicative of the simulated state of the equipment10. As an example, the simulation model155may be a simple model capable of simulating a state of the equipment10in a shorter time than a time spent on an actual operation of the equipment10, for example, a simple physical model having a relatively light processing load or a relatively low-order linear model. Note that, in the present drawing, a case where the simulation model155is built in the control apparatus100is shown as an example. However, the present invention is not limited thereto. Similar to the control model135, the simulation model155may also be stored in an apparatus (for example, on a cloud server) different from the control apparatus100. In addition, the above-described simulator may also be provided to an apparatus different from the control apparatus100.

The stop unit160is configured to stop the control of the control target20by the control model135, based on the simulation result. For example, the stop unit160is configured to determine whether the simulation result supplied from the simulation unit150satisfies a predetermined condition (for example, an abnormality diagnosis condition). When the simulation result indicates occurrence of an abnormality in the equipment10, the stop unit160is configured to notify the control unit140to that effect. In response to this, the control unit140is configured to stop the supply of the operation amount MV (AI) output by the control model135to the control target20. In this way, the stop unit160is configured to stop the control of the control target20by the control model135when the simulation result indicates the occurrence of an abnormality in the equipment10(for example, when it is expected that an abnormality will occur in the equipment10within a few days).

FIG.2shows an example of a flow where the control apparatus100according to the present embodiment stops AI control.

In step210, the control apparatus100acquires state data. For example, the state data acquisition unit110acquires state data indicative of a state of the equipment10provided with the control target20. As an example, the state data acquisition unit110acquires operation data, consumption data, external environment data and the like measured by the sensors provided to the equipment10from the sensors via a network, as the state data. The state data acquisition unit110is configured to supply the acquired state data to the control model generation unit130and the control model135.

In step220, the control apparatus100acquires operation amount data. For example, the operation amount data acquisition unit120acquires operation amount data indicative of an operation amount of the control target20. As an example, the operation amount data acquisition unit120acquires data, which indicates an operation amount MV (AI) that is output by the control model135when AI-controlling the control target20, from the control unit140, as the operation amount data. The operation amount data acquisition unit120is configured to supply the acquired operation amount data to the control model generation unit130. Note that, in the present drawing, a case where the control apparatus100acquires the operation amount data after acquiring the state data is shown as an example. However, the present invention is not limited thereto. The control apparatus100may also acquire the state data after acquiring the operation amount data or may also acquire the state data and the operation amount data at the same time.

In step230, the control apparatus100generates the control model135. For example, the control model generation unit130generates the control model135, which outputs an operation amount corresponding to a state of the equipment10, by machine learning by using the state data and the operation amount data. As an example, the control model generation unit130generates the control model135, which outputs the operation amount MV (AI) corresponding to a state of the equipment10, by reinforcement-learning, as learning data, the state data acquired in step210and the data indicative of the operation amount MV (AI) acquired in step220.

In general, when an agent observes a state of an environment and selects a certain action, the environment changes based on the action. In reinforcement learning, a certain reward is given in association with such change in environment, so that the agent learns selection of a better action (decision-making). In supervised learning, a complete correct answer is given, whereas in reinforcement learning, a reward is given as a fragmentary value based on change in a part of the environment. For this reason, the agent learns so as to select an action that maximizes a total reward in the future. In this way, in reinforcement learning, the agent learns an appropriate action, considering an interaction that an action has on the environment by learning the action, i.e., an action for maximizing a reward that will be obtained in the future.

In the present embodiment, the reward in such reinforcement learning may be an index for evaluating an operation of the equipment10or may be a value determined by a predetermined reward function. As used herein, the function is a mapping having a rule of correlating each element of a certain set and each element of another set on one-to-one correspondence, and may be, for example, a mathematical formula or a table.

The reward function outputs a value (reward value) obtained as a result of evaluating the state of the equipment10indicated by the state data, in response to the input of the state data. As described above, for example, the state data includes the measured value PV measured for the control target20. Therefore, the reward function may be defined as a function in which the reward value becomes higher as the measured value PV is closer to a target value SV (Setting Variable). Here, a function whose variable is an absolute value of a difference between the measured value PV and the target value SV is defined as an evaluation function. That is, as an example, in a case where the control target20is a valve, the evaluation function may be a function whose variable is an absolute value of a difference between the measured value PV, which is an opening degree of the valve actually measured by a sensor, and the target value SV, which is a target opening degree of the valve. The reward function may be a function whose variable is a value of the evaluation function obtained by such evaluation function.

Further, as described above, the state data includes, for example, various values that change depending on an output of the control target20, consumption data, external environment data, and the like, in addition to the measured value PV Therefore, the reward function may be a function that increases or decreases the reward value based on such various values, consumption data, external environment data, and the like. As an example, in a case where there are constraints that should be observed with respect to such various values and consumption data, the reward function may be a function that minimizes the reward value, if such various values and consumption data do not satisfy constraint conditions, in light of the external environment data. Further, in a case where there are targets that are to be aimed with respect to such various values and consumption data, the reward function may be a function that increases the reward value as such various values and consumption data are closer to the targets and decreases the reward value as such various values and consumption data are farther from the targets, in light of the external environment data.

The control model generation unit130acquires the reward value in each learning data, based on such reward function. Then, the control model generation unit130performs reinforcement learning by using each set of learning data and reward value. At this time, the control model generation unit130may perform learning processing by a known method such as a steepest descent method, a neural network, a DQN (Deep Q-Network), a Gaussian process and deep learning. Then, the control model generation unit130learns so that an operation amount whose reward value is higher is preferentially output as a more recommended operation amount. That is, the control model generation unit130is configured to generate the control model135by performing reinforcement learning so that an operation amount whose reward value determined by a predetermined reward function is higher is output as a more recommended operation amount, in response to an input of the state data. Thereby, the model is updated and the control model135is generated.

In step240, the control apparatus100AI-controls the control target20. For example, by supplying the operation amount MV (AI) output by the control model135to the control target20, the control unit140controls the control target20by the control model135machine-learned so as to output the operation amount of the control target20according to the state of the equipment10provided with the control target20. Further, in parallel with this, the control unit140supplies the operation amount MV (AI) output by the control model135to the simulation unit150.

In step250, the control apparatus100executes simulation. For example, the simulation unit150, by using the simulation model155, simulates a state of the equipment10in a case where the operation amount MV (AI) output by the control model135is given to the control target20. As an example, the simulation unit150inputs, to the simulation model155, the operation amount MV (AI) output by the control model135generated in step230, and acquires a plurality of output values output by the simulation model155, as a simulation result. In this way, in parallel with the AI control, the control apparatus100simulates the state of the equipment10in a case where the operation amount MV (AI) output by the control model135is given to the control target20. The simulation unit150is configured to supply the acquired simulation result to the stop unit160.

In step255, the control apparatus100determines presence or absence of occurrence of an abnormality in the equipment10. For example, the stop unit160may store in advance an abnormality diagnosis condition for diagnosing an abnormality in the equipment10. When all of the plurality of output values output by the simulation model155do not satisfy the abnormality diagnosis condition, the stop unit160may infer that no abnormality occurs in the equipment10. In addition, when at least one of the plurality of output values output by the simulation model155satisfies the abnormality diagnosis condition, the stop unit160may infer that an abnormality occurs in the equipment10.

When the simulation result does not indicate occurrence of an abnormality in the equipment10in step255(in a case of No), the control apparatus100returns the processing to step250and continues the flow. Note that, in this case, the AI control in step240continues.

When the simulation result indicates occurrence of an abnormality in the equipment10in step255(in a case of Yes), the control apparatus100advances the processing to step260.

In step260, the control apparatus100stops the AI control. For example, the stop unit160stops the control of the control target20by the control model135, based on the simulation result. As an example, when the simulation result indicates occurrence of an abnormality in the equipment10, the stop unit160notifies the control unit140to that effect. In response to this, the control unit140is configured to stop the supply of the operation amount MV (AI) output by the control model135to the control target20. In this way, the stop unit160stops the control of the control target20by the control model135when the simulation result indicates the occurrence of an abnormality in the equipment10.

In general, machine learning uses input data to determine a parameter of a learning model, which is stochastically obtained and is not theoretically guaranteed. For this reason, abnormal inference data may be output from the learning model. Therefore, the control apparatus100according to the present embodiment simulates, by using the simulation model155, the state of the equipment10in a case where the operation amount MV (AI) output by the control model135is given to the control target20, in parallel with the AI control of the control target20. Then, the control apparatus100stops the AI control, based on the simulation result. Thereby, according to the control apparatus100of the present embodiment, when it is inferred that the equipment10behaves abnormally with the AI control, the AI control can be stopped. Here, it may also be considered to determine whether to stop the AI control, based on whether the operation amount MV (AI) output by the control model135satisfies a predetermined standard. However, such standard is given artificially and empirically, and, it cannot be said that even if the operation amount MV (AI) satisfies such standard, an abnormality always does not occur in the equipment10. Similarly, it cannot be said that even if the operation amount MV (AI) does not satisfy such standard, an abnormality always occurs in the equipment10. In contrast, according to the control apparatus100of the present embodiment, the stop of the AI control is determined, based on the result of simulating the state of the equipment10in a case where the operation amount MV (AI) is given to the control target20, not based on the operation amount MV (AI) itself. Therefore, it is possible to determine the stop of the AI control, based on a basis closer to the actual operation.

Further, in the control apparatus100according to the present embodiment, the simulation model155used for simulation may be a simple model, as compared to the actual equipment10. Thereby, according to the control apparatus100of the present embodiment, since the state of the equipment10can be simulated in a shorter time than a time spent on an actual operation of the equipment10, the AI control can be stopped prior to occurrence of an abnormality in the actual equipment10.

FIG.3shows an example of a block diagram of the control apparatus100according to a first modified embodiment of the present embodiment, together with the equipment10provided with the control target20. InFIG.3, the members having same functions and configurations as those inFIG.1are denoted with the same reference signs, and descriptions thereof are omitted, except differences to be described below. In the control apparatus100according to the above-described embodiment, the case where the stop of AI control is automatically determined based on the simulation result has been shown as an example. However, in the control apparatus100according to the first modified embodiment, the simulation result is output, and the AI control is stopped based on an instruction from an operator or the like who has examined the simulation result. The control apparatus100according to the first modified embodiment further comprises an output unit310and an input unit320.

In the control apparatus100according to the present modified embodiment, the simulation unit150is configured to supply a simulation result to the output unit310, in addition to the stop unit160. The output unit310is configured to output the simulation result. For example, the output unit310may also be configured to output the simulation result by displaying the same on a monitor, to output the simulation result by printing the same out, or to output the simulation result by data-transmitting the same to another apparatus.

The input unit320is configured to receive a user input from an operator or the like who has examined the simulation result, in response to the output of the simulation result. The input unit320is configured to supply, to the stop unit160, a user-input instruction from the operator.

The stop unit160is configured to determine to stop the AI control when the instruction supplied from the input unit320indicates that the AI control is to be stopped. That is, the stop unit160is configured to stop the control of the control target20by the control model135when the stop unit acquires an instruction to stop the control in response to the output of the simulation result.

FIG.4shows an example of a flow where the control apparatus100according to the first modified embodiment of the present embodiment stops AI control. InFIG.4, the same processing as those inFIG.2is denoted with the same reference signs, and descriptions thereof are omitted, except differences to be described below. In the present flow, steps410and420are provided, instead of step255.

In step410, the control apparatus100outputs a simulation result. For example, the output unit310acquires a result of the simulation performed in step250by the simulation unit150and displays the simulation result on the monitor, thereby outputting the simulation result.

In step420, the control apparatus100determines whether the stop of the AI control has been instructed. For example, the stop unit160determines whether an instruction to stop the AI control has been acquired from the operator or the like who has examined the simulation result, via the input unit320. When an instruction to stop the AI control has not been acquired in step420(in a case of No), the control apparatus100returns the processing to step250and continues the flow. When an instruction to stop the AI control has been acquired in step420(in a case of Yes), the control apparatus100advances the processing to step260. Then, the stop unit160notifies the control unit140that the AI control is to be stopped. In response to this, the control unit140is configured to stop the supply of the operation amount MV (AI) output by the control model135to the control target20. In this way, the stop unit160stops the control of the control target20by the control model135when the stop unit acquires an instruction to stop the control in response to the output of the simulation result.

In this way, the control apparatus100according to the first modified embodiment outputs the simulation result, and stops the AI control, based on an instruction from the operator or the like who has examined the simulation result. Thereby, according to the control apparatus100of the first modified embodiment, it is possible to reflect an intention of the operator or the like when stopping the AI control.

Note that, in the above descriptions, the case where the control apparatus100executes steps410and420instead of step255has been shown as an example. However, the present invention is not limited thereto. The control apparatus100according to the first modified embodiment may execute steps410and420, in addition to step255. At this time, the control apparatus100may stop the AI control when either the stop instruction by the operator or the like or the automatic determination by a computer is satisfied. Instead of this, the control apparatus100may stop the AI control only when both the stop instruction by the operator and the automatic determination by the computer are satisfied. In this case, for example, when the control apparatus100outputs a simulation result indicating that an abnormality occurs in the equipment10to notify the operator or the like of an occurrence of the abnormality and acquires a stop instruction from the operator or the like in response to the notification, the control apparatus may stop the AI control. Thereby, according to the control apparatus100according to the first modified embodiment, the AI control can be stopped by using both automatic determination by the computer and manual determination by the operator.

FIG.5shows an example of a block diagram of the control apparatus100according to a second modified embodiment of the present embodiment, together with the equipment10provided with the control target20. InFIG.5, the members having same functions and configurations as those inFIG.1are denoted with the same reference signs, and descriptions thereof are omitted, except differences to be described below. In the control apparatus100according to the above-described embodiment, the case where when the control model135outputs the operation amount MV (AI), the state of the equipment10in a case where the operation amount MV (AI) is given to the control target20is always simulated has been shown as an example. However, in the control apparatus100according to the second modified embodiment, a frequency of simulating the state of the equipment10is adjusted. The control apparatus100according to the second modified embodiment further comprises a frequency adjustment unit510.

The frequency adjustment unit510is configured to trigger a simulation by the simulation unit150. For example, the frequency adjustment unit510is configured to instruct the simulation unit150to simulate a state of the equipment10when a simulation timing has arrived. In response to this, the simulation unit150is configured to simulate a state of the equipment10. At this time, the frequency adjustment unit510may be configured to be able to adjust a frequency of simulating a state of the equipment10. For example, the frequency adjustment unit510may be configured to reduce the frequency of simulation as an elapsed time since the control of the control target20by the control model135is started becomes longer.

FIG.6shows an example of a flow where the control apparatus100according to the second modified embodiment of the present embodiment stops AI control. InFIG.6, the same processing as those inFIG.2is denoted with the same reference signs, and descriptions thereof are omitted, except differences to be described below. In the present flow, steps610and620are further provided.

In step610, the control apparatus100(for example, the frequency adjustment unit510) determines whether it is a simulation timing. Note that, an initial value of such simulation timing and an initial value of a simulation interval may be stored in advance. When it is determined in step610that it is not the simulation timing (in a case of No), the control apparatus100returns the processing to step610and continues the flow.

When it is determined in step610that it is the simulation timing (in a case of Yes), the control apparatus100triggers simulation by the simulation unit150. For example, the frequency adjustment unit510is configured to instruct the simulation unit150to simulate a state of the equipment10when a simulation timing has arrived. In response to this, the simulation unit150simulates a state of the equipment10in step250.

When the simulation result does not indicate occurrence of an abnormality in the equipment10in step255(in a case of No), the control apparatus100advances the processing to step620.

In step620, the control apparatus100reduces the simulation frequency. For example, the frequency adjustment unit510updates the simulation interval by adding a fixed length to the simulation interval stored in advance. That is, the frequency adjustment unit510reduces the frequency of simulation by extending an interval until a next simulation timing. In this way, when the elapsed time since the start of the AI control without stopping the AI control becomes longer, the frequency adjustment unit510determines that the AI control has become stable, and can reduce the simulation frequency. Then, the control apparatus100returns the processing to step610and continues the flow. In this way, in the control apparatus100according to the second modified embodiment, the frequency adjustment unit510may be configured to be able to adjust the frequency of simulating a state of the equipment10. More specifically, the frequency adjustment unit510may be configured to reduce the frequency of simulation as the elapsed time since the control of the control target20by the control model135is started becomes longer.

The control apparatus100according to the second modified embodiment can adjust the frequency of simulating a state of the equipment10. In particular, the control apparatus100according to the second modified embodiment is configured to reduce the frequency of simulation as the elapsed time since the AI control is started becomes longer. That is, the control apparatus100according to the second modified embodiment is configured to frequently execute the simulation immediately after the start of the AI control, and to reduce the frequency of executing the simulation as a long time elapses after the AI control is started. Thereby, according to the control apparatus100of the second modified embodiment, since the frequency of simulation can be adjusted depending on actual performance of the AI control, a processing load of the simulation in the control apparatus100can be reduced.

FIG.7shows an example of a block diagram of the control apparatus100according to a third modified embodiment of the present embodiment, together with the equipment10provided with the control target20. InFIG.7, the members having same functions and configurations as those inFIG.1are denoted with the same reference signs, and descriptions thereof are omitted, except differences to be described below. In the control apparatus100according to the above-described embodiment, the case where an output of the control unit140(i.e., an output of the control model135) and the control target20are directly connected has been shown as an example. However, in the control apparatus100according to the third modified embodiment, it is possible to switch whether to input or shut off an output of the control model135with respect to the control target20. The control apparatus100according to the third modified embodiment further comprises a switching unit710.

The switching unit710has one end connected to an output of the control unit140(i.e., an output of the control model135), and the other end connected to an input of the control target20. The switching unit710is configured to switch whether to input or shut off the output of the control model135with respect to the control target20. Such switching unit710may be configured by, for example, a switch configured to open and close an electric circuit, and in particular, may be configured by a physical switch configured to realize an open state of the electric circuit by a physical mechanism. In the control apparatus100according to the third modified embodiment, the stop unit160is configured to shut off the switching unit710when stopping the control of the control target20by the control model135.

In this way, the control apparatus100according to the third modified embodiment can switch whether to input or shut off the output of the control model135with respect to the control target20. Thereby, according to the control apparatus100of the third modified embodiment, it is possible to physically shut off the output of the control model135from being supplied to the control target20when stopping the AI control, and to prevent the output of the control model135from being erroneously input to the control target20.

FIG.8shows an example of a block diagram of the control apparatus100according to a fourth modified embodiment of the present embodiment, together with the equipment10provided with the control target20. InFIG.8, the members having same functions and configurations as those inFIG.1are denoted with the same reference signs, and descriptions thereof are omitted, except differences to be described below. In the control apparatus100according to the above-described embodiment, the case where the AI control is simply stopped based on the simulation result has been shown as an example. However, in the control apparatus100according to the fourth modified embodiment, the control apparatus100is configured to instruct the control target20to switch to control by another control means when stopping the AI control. The control apparatus100according to the fourth modified embodiment further comprises an instruction unit810.

In the fourth modified embodiment, it is assumed that the control target20can switch between feedback control by an operation amount MV (FB) given from a further controller (not shown) and AI control by an operation amount MV (AI) given from the control model.

In the control apparatus100according to the fourth modified embodiment, when stopping the AI control, the stop unit160is configured to notify the instruction unit810to that effect.

In response to this, the instruction unit810is configured to instruct the control target20to switch to control by another control means when stopping the control of the control target20by the control model135. For example, when the control target20is capable of the feedback control, in addition to the AI control, the instruction unit810is configured to instruct the control target20to switch to the feedback control.

The control apparatus100according to the fourth modified embodiment is configured to instruct the control target20to switch to control by another control means when stopping the AI control. Thereby, according to the control apparatus100of the fourth modified embodiment, even when the AI control is stopped, the control of the control target20can be continued by another control means.

Various embodiments of the present invention may be described with reference to flowcharts and block diagrams whose blocks may represent (1) steps of processes in which operations are executed or (2) sections of apparatuses responsible for performing operations. Certain steps and sections may be implemented by dedicated circuitry, programmable circuitry supplied with computer-readable instructions stored on computer-readable media, and/or processors supplied with computer-readable instructions stored on computer-readable media. The dedicated circuitry may include a digital and/or analog hardware circuit, or may include an integrated circuit (IC) and/or a discrete circuit. The programmable circuitry may include a reconfigurable hardware circuit including logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, a memory element such as a flip-flop, a register, a field programmable gate array (FPGA) and a programmable logic array (PLA), and the like.

Computer-readable media may include any tangible device that can store instructions to be executed by a suitable device, and as a result, the computer-readable storage medium having the instructions stored thereon comprises an article of manufacture including instructions that can be executed to provide means for performing operations specified in the flowcharts or block diagrams. Examples of computer-readable media may include an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, and the like. More specific examples of computer-readable media may include a floppy (registered trademark) disk, a diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory (registered trademark)), an electrically erasable programmable read-only memory (EEPROM), a static random access memory (SRAM), a compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a BLU-RAY (registered trademark) disk, a memory stick, an integrated circuit card, and the like.

Computer-readable instructions may include assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code described in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk (registered trademark), JAVA (registered trademark) and C++, and a conventional procedural programming language such as a ‘C’ programming language or similar programming languages.

Computer-readable instructions may be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing apparatus, or to programmable circuitry, locally or via a local area network (LAN), wide area network (WAN) such as the Internet, etc., and the computer-readable instructions may be executed to provide means for performing operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, and the like.

FIG.9shows an example of a computer9900where a plurality of aspects of the present invention may be entirely or partially embodied. A program that is installed in the computer9900can cause the computer9900to function as or execute operations associated with the apparatus of the embodiment of the present invention or one or more sections of the apparatus, and/or cause the computer9900to execute the process of the embodiment of the present invention or steps thereof. Such a program may be executed by a CPU9912so as to cause the computer9900to execute a specific operation associated with some or all of the flowcharts and the blocks in the block diagrams described herein.

The computer9900according to the present embodiment includes the CPU9912, a RAM9914, a graphic controller9916and a display device9918, which are mutually connected by a host controller9910. The computer9900also includes input and output units such as a communication interface9922, a hard disk drive9924, a DVD drive9926and an IC card drive, which are connected to the host controller9910via an input and output controller9920. The computer also includes legacy input and output units such as a ROM9930and a keyboard9942, which are connected to the input and output controller9920via an input and output chip9940.

The CPU9912is configured to operate according to programs stored in the ROM9930and the RAM9914, thereby controlling each unit. The graphic controller9916is configured to acquire image data generated by the CPU9912on a frame buffer or the like provided in the RAM9914or in itself, and to cause the image data to be displayed on the display device9918.

The communication interface9922is configured to communicate with other electronic devices via a network. The hard disk drive9924is configured to store programs and data that are used by the CPU9912within the computer9900. The DVD drive9926is configured to read programs or data from a DVD-ROM9901, and to provide the hard disk drive9924with the programs or data via the RAM9914. The IC card drive is configured to read programs and data from an IC card, and/or to write programs and data into the IC card.

The ROM9930is configured to store therein a boot program or the like that is executed by the computer9900at the time of activation, and/or a program depending on the hardware of the computer9900. The input and output chip9940may also be configured to connect various input and output units to the input and output controller9920via a parallel port, a serial port, a keyboard port, a mouse port and the like.

A program is provided by a computer-readable medium such as the DVD-ROM9901or the IC card. The program is read from the computer-readable medium, is installed into the hard disk drive9924, the RAM9914or the ROM9930, which are also examples of the computer-readable medium, and is executed by the CPU9912. Information processing described in these programs is read into the computer9900, resulting in cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be constituted by realizing an operation or processing of information according to a use of the computer9900.

For example, when communication is performed between the computer9900and an external device, the CPU9912may be configured to execute a communication program loaded onto the RAM9914to instruct the communication interface9922for communication processing, based on processing described in the communication program. The communication interface9922is configured, under control of the CPU9912, to read transmission data stored on a transmission buffer processing area provided in a recording medium such as the RAM9914, the hard disk drive9924, the DVD-ROM9901or the IC card, and to transmit the read transmission data to a network or to write reception data received from the network to a reception buffer processing area or the like provided on the recording medium.

In addition, the CPU9912may be configured to cause all or a necessary portion of a file or a database, which has been stored in an external recording medium such as the hard disk drive9924, the DVD drive9926(DVD-ROM9901) and the IC card, to be read into the RAM9914, thereby executing various types of processing on the data on the RAM9914. Next, the CPU9912is configured to write the processed data back to the external recording medium.

Various types of information, such as various types of programs, data, tables, and databases, may be stored in the recording medium and may be subjected to information processing. The CPU9912may be configured to execute, on the data read from the RAM9914, various types of processing including various types of operations, processing of information, conditional judgment, conditional branching, unconditional branching, search and replacement of information and the like described in the present disclosure and specified by instruction sequences of the programs, and to write a result back to the RAM9914. In addition, the CPU9912may be configured to search for information in a file, a database, etc., in the recording medium. For example, when a plurality of entries, each having an attribute value of a first attribute associated with an attribute value of a second attribute, is stored in the recording medium, the CPU9912may be configured to search for an entry having a designated attribute value of the first attribute that matches a condition from the plurality of entries, and to read the attribute value of the second attribute stored in the entry, thereby acquiring the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

The programs or software modules described above may be stored in the computer-readable medium on the computer9900or near the computer9900. In addition, a recording medium such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable medium, thereby providing the programs to the computer9900via the network.

While the present invention has been described using the embodiments, the technical scope of the present invention is not limited to the above-described embodiments. It is apparent to persons skilled in the art that various alterations and improvements can be added to the above-described embodiments. It is also apparent from the scope of the claims that the embodiments added with such alterations or improvements can be included in the technical scope of the present invention.

The operations, procedures, steps, stages and the like of each process performed by an apparatus, system, program, and method shown in the claims, embodiments, or diagrams can be performed in any order as long as the order is not indicated by ‘prior to,’ before,′ or the like and as long as the output from a previous process is not used in a later process. Even if the process flow is described using phrases such as ‘first’ or ‘next’ in the claims, embodiments, or diagrams, it does not necessarily mean that the process must be performed in this order.

EXPLANATION OF REFERENCES