Abstract
In this paper, a novel alleviating computation adaptive fuzzy control scheme is presented for a class of uncertain stochastic nonlinear pure-feedback systems. Different from the existing results that are based on traditional back-stepping schemes as well as approximation technique of fuzzy logic systems (FLSs) for stochastic nonlinear systems, this new approach assumes that the norm of optimal approximation parameter vector of FLSs is bounded by unknown parameter. At each design step of this new approach, fewer bounded adaptive parameters need to be adjusted. Thus, this new approach can alleviate the online computation burden and improve the robust control performance. Meanwhile, combining Lyapunov theorem analysis, it is proven that all the signals in the closed-loop system are uniformly ultimately bounded (UUB) and the tracking error can converge to a small neighborhood of zero. The good performance of this approach is well demonstrated in a simulation example.
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