The application of wavelet neural networks to nonlinear predictive control

An identification and predictive control strategy for nonlinear processes based on orthogonal wavelet basis function networks is proposed. In this paper, a wavelet neural network with a linear least squares learning algorithm is developed for a process model. This can be used with nonlinear programming to implement nonlinear model predictive control strategy. Since simplified online optimization method has been developed, this control strategy is very easy to implement. Using the proposed identification and control strategy, a control system of bilinear process is simulated. It shows excellent performance superior to a standard PID controller for the nonlinear processes