文件名称:CONTROLLER-PARAMETERS-TUNING-USING-GENETIC-ALGORI
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The paper deals with a controller design for the nonlinear processes using genetic
algorithm and neural model. The aim was to improve the control performance using
genetic algorithm for optimal PID controller tuning. The plant model has been
identified via an artificial neural network from measured data. The genetic algorithm
represents an optimisation procedure, where the cost function to be minimized
comprises the closed-loop simulation of the control process and a selected
performance index evaluation. Using this approach the parameters of the PID
controller were optimised in order to become the required behaviour of the control
process. Testing of quality control process was realized in simulation environment of
Matlab Simulink on selected types of nonlinear dynamic processes.-The paper deals with a controller design for the nonlinear processes using genetic
algorithm and neural model. The aim was to improve the control performance using
genetic algorithm for optimal PID controller tuning. The plant model has been
identified via an artificial neural network from measured data. The genetic algorithm
represents an optimisation procedure, where the cost function to be minimized
comprises the closed-loop simulation of the control process and a selected
performance index evaluation. Using this approach the parameters of the PID
controller were optimised in order to become the required behaviour of the control
process. Testing of quality control process was realized in simulation environment of
Matlab Simulink on selected types of nonlinear dynamic processes.
algorithm and neural model. The aim was to improve the control performance using
genetic algorithm for optimal PID controller tuning. The plant model has been
identified via an artificial neural network from measured data. The genetic algorithm
represents an optimisation procedure, where the cost function to be minimized
comprises the closed-loop simulation of the control process and a selected
performance index evaluation. Using this approach the parameters of the PID
controller were optimised in order to become the required behaviour of the control
process. Testing of quality control process was realized in simulation environment of
Matlab Simulink on selected types of nonlinear dynamic processes.-The paper deals with a controller design for the nonlinear processes using genetic
algorithm and neural model. The aim was to improve the control performance using
genetic algorithm for optimal PID controller tuning. The plant model has been
identified via an artificial neural network from measured data. The genetic algorithm
represents an optimisation procedure, where the cost function to be minimized
comprises the closed-loop simulation of the control process and a selected
performance index evaluation. Using this approach the parameters of the PID
controller were optimised in order to become the required behaviour of the control
process. Testing of quality control process was realized in simulation environment of
Matlab Simulink on selected types of nonlinear dynamic processes.
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CONTROLLER PARAMETERS TUNING USING GENETIC ALGORITHM AND NEURAL MODEL.pdf