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Function approximation accuracy and computational cost are two major concerns in approximation-based adaptive fuzzy control. In this paper, a model reference composite learning fuzzy control strategy is proposed for a class of affine nonlinear systems with functional uncertainties. In the proposed approach, a modified modeling error that utilizes data recorded online is defined as a prediction error,...
Parameter convergence is desirable in adaptive control as it brings several attractive features, including accurate online identification, exponential tracking and robust adaptation without parameter drift. However, a strong persistent-excitation (PE) condition has to be satisfied to guarantee parameter convergence in the conventional adaptive control. This paper proposes a novel composite learning...
Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with direct and indirect adaptive control techniques. However, the condition of persistent excitation (PE) still has to be satisfied to guarantee parameter convergence in CAC. This paper proposes a novel model reference composite...
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