Indirect self-tuning control of a nonlinear non-minimum phase system

Stability analysis of indirect self-tuning control algorithm is relatively complex, since time-varying operation should be dealt with. In this paper, for a class of discrete time nonlinear non-minimum systems, by using multiple models and neural networks, an indirect self-tuning control method based on a one-step-ahead optimal weighting control scheme is proposed. In the self-tuning control method, two new identification algorithms are first presented. The properties of the identification algorithms are provided. The bounded-input-bounded-output (BIBO) stability of the closed-loop system is proved, and the performance is analyzed. To illustrate the effectiveness of the proposed method, simulations are conducted.