Identification of continuous-time nonlinear systems via local linear equations united by automatic choosing function and genetic algorithm

Abstract

This paper deals with an identification method of continuous-time nonlinear systems using a model expanded by a basis of automatic choosing functions (ACF). Higher order derivatives of input and output data are estimated by a delayed state variable filter, or the Butterworth filter. An unknown nonlinear function to be estimated is approximately described by local linear equations united by ACF. The resulting model is linear in the parameters, which are estimated by the least-squares method. The model structure and Butterworth filter are properly determined by a genetic algorithm. Simulation results are shown to demonstrate the effectiveness of the proposed method.