A Novel Model-based Predictive Wavelet Network Strategy for Control of Spark Injection Engines

An application of wavelet neural network/wavenet in control problems of nonlinear systems is investigated in this study. A wavelet neural network is constructed as an alternative architecture to a neural network to approximate a nonlinear system. Based on approximation capability of wavelet network, a suitable adaptive control law and parameter updating algorithm as applied to nonlinear system uncertainty estimation are developed. It is shown that using wavelet neural networks, an effective uncertainty estimation and control strategy can be obtained. This method improves plant performance effectively and provides robustness against variations caused by changes in operating points of the system. Simulation results show the superiority of the method.