INDIRECT ADAPTIVE FUZZY CONTROL FOR A CLASS OF NONLINEAR SYSTEMS

This paper presents two indirect adaptive fuzzy control schemes for nonlinear uncertain stable plants with unmeasurable states. A discrete-time T-S fuzzy model is employed as a dynamic model of an unknown plant. Based on the global T-S model, a feedback linearization controller is designed and applied to both the model and the plant. Based on local affine models, local integral controllers are first designed and then fuzzily combined to provide a nonlinear controller. Parameters of the model are updated on-line to allow for partially unknown and time-varying plants. Stability analysis shows that both adaptive controller guarantees the boundedness of all the closed-loop signals and achieves bounded tracking error. In the ideal case where there is no modelling error and the signal for parameter learning is persistently exciting, perfect tracking is ensured. The effectiveness of the two schemes is verified by simulation examples and their performance is compared and discussed.