Improved Piecewise Linear Approximation of Nonlinear Functions in Hybrid Control

摘要

The paper addresses the issue of the PWA approximation of nonlinear functions in hybrid modeling. We propose an original approach to PWA approximation of common nonlinear functions of one and more variables by orthogonal activation function based neural network (OAF NN). Complexity of the PWA system is the basic limitation in model predictive control design. We present a universal linearization method for generating PWA model of a common nonlinear system from process data. The presented method gives good results in precision in proportion to the number of linearization points. The method allows linearization of a nonlinear dynamical system in few number of linearization points for the purpose of hybrid modeling and model predictive control. The proposed method was successfully verified on many case studies for SISO and MIMO systems.