Process control method and system for performing control of a controlled system by use of a neural network

A control system having a controller for controlling a controlled system such that a controlled variable is brought into conformity with a desired value, comprising:

• a tuning system for the controller, wherein said tuning system comprises:
• a neural network having a plurality of mutually-connectable units and capable of obtaining an output signal corresponding to an input signal in accordance with the state of connection among the units,
• a means for receiving information which contains characteristics of at least one of input/output variables for a combined controlling-controlled system, comprising in combination the controller and the controlled system, and input/output variables for the controlled system, and
• a means for inputting the information with the characteristics contained therein to the neural network to output a control parameter for the controller;
• and
• a parameter tuning system for tuning the control parameter based on said input/output variables and outputting a tuned control parameter to the controller.

The control system having a controller for controlling a controlled system such that a controlled variable is brought into conformity with a desired value, comprising:

• a tuning system for the controller, wherein said tuning system comprises:
• a neural network having a plurality of mutually-connectable units and capable of obtaining an output signal corresponding to an input signal in accordance with the state of connection among the units,
• means for receiving information which contains characteristics of at least one of input/output variables for a combined controlling-controlled system, said combined controlling-controlled system comprising in combination a controller and a controlled system, and input/output variables for the controlled system, and
• means for inputting the information with the characteristics contained therein to the neural network to output a control parameter for the controller;
• a tuning rule learning system for causing the neural network, which is adapted to tune the control parameter of the controller, to learn a tuning rule, wherein said tuning rule learning system comprises:
• a second neural network different form the first-mentioned neural network,
• means for developing models of the controller and controlled system,
• means for determining a time response on at least one characteristic of each of the models to obtain the information with the characteristics of the input/output variable contained therein,
• means for determining a control parameter capable of providing optimal control results conforming with the characteristics of each of the models, and
• means for causing the second neural network to learn by using the information with the characteristics of the input/output variable and the control parameter as learning input data and learning teacher data, respectively;
• and
• wherein the results of the learning by the second neural network are transferred to the first neural network and are used therein.

A method for controlling a controlled system by a controller so as to bring a controlled variable into conformity with a desired value, comprising the steps of:

• receiving information which contains characteristics of at least one of input/output variables for the controlled system;
• inputting the information to a neural network;
• identifying a model of the controlled system by using a learning function of the neural network;
• and
• determining a manipulated variable by using the model, whereby the controlled system is controlled;
• wherein the neural network is caused to learn by using time-series signals of an output variable and time-series signals of an input variable as the information with the characteristics of the input/output variable of the controlled system, the time-series signals of the output variable as learning input data and the time-series signals of the input variable as learning teacher data, whereby a reverse system model of the controlled system is identified;
• wherein an output variable of a reference model of a control system, which comprises in combination the controlled system and a controller for controlling the controlled system is inputted to the neural network with the reverse system model identified therein and the controller is tuned to bring time-series signals of an output variable of the controller into conformity with the time-series signals of the input variable of the controlled system;
• wherein an additional neural network different from the neural network with the reverse system model identified therein is used as the controller.

A method for predictively controlling a controlled system to bring a controlled variable into conformity with a desired value, said method comprising the steps of:

• receiving input/output variables of said controlled system as input signals for a neural network, to make the neural network learn the output variable of the controlled system as teacher data, so that a model is identified which is to output an estimated value of the output variable of the controlled system in the near future when the signals of the input/output variables of the controlled system are inputted;
• inputting the input/output variables of the controlled system at the present time into said model, to make said model predict and output the estimated value of the output variable of the controlled system in the near future;
• determining one or more manipulated variables by tuning said estimated value based on the input/output variables of the controlled system and said desired value;
• and
• outputting said one or more manipulated variables to the controlled system.

A method for predictively controlling a controlled system having nonlinear characteristics, to bring a controlled variable into conformity with a desired value, said method comprising the steps of:

• receiving input/output variables of the controlled system as input signals for a neural network, to make the neural network learn the output variable of the controlled system as teacher data, so that a nonlinear model is identified which is to output the output variable of the controlled system in the near future wherein signals of the input/output variables of the controlled system are inputted;
• developing a linear model using said nonlinear model;
• and
• inputting the input/output variables of the controlled system at the present time into said linear model, to make said model predict and output an estimated value of the output variable of the controlled system in the near future, and determining one or more manipulated variables based upon said estimated value and said desired value thereby controlling the controller system.

A control system having a predictive control system for predictively controlling a controlled system having nonlinear characteristics, by determining one or more manipulated variables based on a deviation between a controlled variable and desired variable, said control system comprising:

• a nonlinear model identification system having a neural network, which receives the input/output variable of the controlled system as input signal for a neural network, to make the neural network learn the output variable of the controlled system as teacher data, so that a nonlinear model is identified which is to output the output variable of the controlled system when signals of the input/output variables of the controlled system are inputted;
• a linear model identification system for developing a linear model using said nonlinear model;
• and
• a controlled variable predictive system which receives the manipulated variables and the controlled variable of the controlled system at the present time using the neural network which identifies the model, to predict the controlled variable of the controlled system in the near future to output an estimated value of the controlled variable.