Adaptive Fuzzy Decentralized Sampled-Data Control for Large-Scale Nonlinear Systems

Abstract

This article proposes a sampled-data adaptive fuzzy decentralized output feedback control method for the uncertain nonstrict feedback large-scale interconnected systems whose nonlinear functions are completely unknown and whose states are unavailable for control design. With the help of fuzzy logical systems in identifying the unknown nonlinear functions, and by considering the sampling strategy, a novel sampled-data nonlinear state observer has been designed to approximate the immeasurable state variables, which only contains the output sampling information of the system to be controlled. Furthermore, to avoid the problem of “explosion of complexity” caused by the frequently using the derivative of the virtual controller, the nonlinear filter has been considered under the strategy of the sampled data. Based on Lyapunov theory, a sampled-data adaptive fuzzy decentralized output feedback backstepping control strategy was proposed under the framework of recursive design and sampled-data strategy. The designed sampled-data controller is able to guarantee that all closed-loop signals are semiglobally uniformly ultimately bounded. Two simulation examples are eventually provided to validate the effectiveness of the theoretical findings.