Stable model reference adaptive fuzzy control of a class of nonlinear systems

Abstract

In this paper, we propose a new adaptive fuzzy control scheme called model reference adaptive fuzzy control (MRAFC). The MRAFC scheme employs a reference model to provide closed-loop performance feedback for generating or modifying a fuzzy controller's knowledge base. The MRAFC scheme grew from ideas in conventional model reference adaptive control (MRAC). The MRAFC scheme is developed to perform adaptive feedback linearization to a class of nonlinear systems. A class of fuzzy controllers, which can be expressed in an explicit form, is used as the primary controller. Based on Lyapunov's second method, we have developed MRAFC schemes and derived fuzzy rule adaptive laws. Hence, not only the stability of the system can be assured but also the performance, such as the issues of robustness and parameter convergence, of the MRAFC system can be analyzed explicitly. We showed that in the case of no modeling error, the state error converges to zero asymptotically. In the case that persistent excitation is satisfied, we showed that the MRAFC system is asymptotically stable. By considering the periodic signal as reference input signal, we showed that the square wave can make the MRAFC system be persistently excited. The feasibility of applying these techniques has been demonstrated by considering the control of an inverted pendulum in following a reference model response.