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.