Abstract
This paper presents an indirect adaptive fuzzy control scheme 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 this model, a feedback linearization controller is designed and applied to both the model and the plant. Parameters of the model are updated on-line to allow for partially unknown and time- varying plants. Stability analysis shows that the 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 method is verified by simulation examples.
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