A self tuning PID controller using Wavelet Networks

In this paper a self tuning adaptive PID control scheme for nonlinear systems is proposed using wavelet networks. The auto tuner consists of a discrete PID controller and a proposed new wavelet network structure called dynamic wavelet network (DWN). The DWN consists of a static feedforward wavelet network in cascade with an autoregressive moving average (ARMA) model. The learning strategy for the wavelet network and PID controller is based on gradient descent. A recursive algorithm is developed to update the weights of the DWN and the parameters of the ARMA model. The performance of the proposed controller is demonstrated via extensive numerical simulations. The results are applied to a typical servomechanism with an input saturation as a hard nonlinearity, showing the feasibility of the proposed adaptive control system.