An improved stable adaptive fuzzy control method

Stable adaptive fuzzy control is a self-tuning concept for fuzzy controller s that uses a Lyapunov-based learning algorithm, thus guaranteeing stabilit y of the system plant-controller-learning algorithm and convergence of the plant output to a given reference signal, In this paper, two new methods fo r stable adaptive fuzzy control are presented. The first method is an exten sion of an existing concept: it is shown that a major drawback of that conc ept, the necessity for new adaptation at every change of the reference sign al, can be avoided by a simple modification. The main focus of the paper is on the presentation of a second method, which extends the applicability of stable adaptive fuzzy control to a broader class of nonlinear plants; this is achieved by an improved controller structure adopted from the neural ne twork domain. Performance and limitations of the proposed methods, as well as some practical design aspects, are discussed and illustrated with simula tion results.