Nonlinear adaptive control using multiple models and dynamic neural networks

Abstract

The adaptive control of nonlinear dynamic system using multiple models has emerged as a mathematically attractive and practically available method. It can be used to improve the transient response of adaptive control. In this paper, parallel dynamic neural networks (PDNNs) are used to identify the unknown dynamic system. Three kinds of combinations of adaptive model and fixed model are established to set up multiple model adaptive control (MMAC) by using an effective switching scheme. The stability and convergence of MMAC using multiple PDNNs are discussed by means of Lyapunov-like analysis. Considering the unmodeled dynamics, the new learning law ensures that the identification error converges to a bounded zone, and the proposed MMAC with PDNNs can improve the control property greatly. By simulation studies, the results of different combinations of fixed and adaptive models are compared, and the effectiveness of the proposed method can be seen.