A design of nonlinear PID controllers 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 the estimates is considered. According to the newly proposed scheme, it is possible to employ to nonlinear systems. Finally, the behavior of the newly proposed control scheme is investigated on a numerical simulation example.