Precise Model Predictive Control of Multivariable Uncertain Switched Systems Using Fuzzy-Wavelet Neural Networks
In this paper, a novel method for model predictive control of switched multivariable systems under arbitrary switching signals is proposed. In this method, an accurate estimation algorithm based on Fuzzy-Wavelet Neural Networks is employed which is proposed for multivariable systems. Based on estimation of active switched dynamical mode, predicted sliding functions are calculated and according to corresponding candidate Lyapunov functions defined for various degrees of freedom, stabilizing MPC constraints are obtained. Existing methods in control of switched systems are based on worst-case switching configurations which result in conservatism of controller. In the proposed method, conservatism is eliminated based on incorporation of active switched dynamics which in turn leads to smaller control inputs and more precise tracking response.
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