Self-tuning PID control using recurrent wavelet neural networks

This paper presents a novel self-tuning PID control using recurrent wavelet neural networks (RWNN-PID) for a class of highly nonlinear discrete-time time-delay systems. The three-term parameters of the self-tuning PID controller are tuned based on the RWNN, in order to achieve setpoint tracking and eliminate any error caused by step disturbances. Numerical simulations for controlling two highly nonlinear process show disturbance rejection and setpoint tracking performance of the proposed control method, thus clearly indicating effectiveness and merit of the proposed method.