Abstract
Abstract A novel adaptive robust fuzzy tracking control (ARFTC) algorithm is proposed for a class of nonlinear systems with uncertain system and gain functions, which are all the unstructured (or non-repeatable) state-dependent functions coming from modelling errors and external disturbances. The Takagi-Sugeno type fuzzy logic systems are used to approximate uncertain functions and a systematic procedure is developed for synthesis of ARFTC by use of small gain approach and dissipative system theory. The algorithm preserves three advantages: (1) the asymptotic stability of adaptive control in the presence of unstructured uncertainties can be guaranteed, (2) possible controller singularity problem in some of existing adaptive control schemes using feedback linearization techniques can be avoided, (3) the adaptive mechanism with minimal learning parameterizations can be obtained. The effectiveness of the proposed ARFTC for the tracking control ofpole-balancing robots is demonstrated though numerical examples.
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