A design of nonlinear PID control systems with a neural-net based system estimator

PID control schemes based on the classical control theory, have been widely used for various process control systems for a long time. However, since such processes have nonlinear properties, it is difficult to determine ´optimal´ PID parameters. In this paper, a system identification scheme by using a neural network is proposed. Furthermore, a PID control scheme based on estimates is considered. According to the newly proposed scheme, system parameters are first estimated by the neural network. The PID control parameters are calculated by using the estimates which are generated by the neural network. By this procedure, the PID control scheme can be employed to the nonlinear systems. Finally, the behavior of the newly proposed control scheme is investigated on a numerical simulation example.