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论文类型： 期刊论文

发表时间： 2017-10-01

发表刊物： IEEE TRANSACTIONS ON CYBERNETICS

收录刊物： Scopus、SCIE、EI

卷号： 47

期号： 10,SI

页面范围： 3088-3099

ISSN号： 2168-2267

关键字： High-order systems; neural network approximation; stochastic systems; switched systems

摘要： This paper deals with adaptive neural tracking control design for a class of switched high-order stochastic nonlinear systems with unknown uncertainties and arbitrary deterministic switching. The considered issues are: 1) completely unknown uncertainties; 2) stochastic disturbances; and 3) high-order nonstrict-feedback system structure. The considered mathematical models can represent many practical systems in the actual engineering. By adopting the approximation ability of neural networks, common stochastic Lyapunov function method together with adding an improved power integrator technique, an adaptive state feedback controller with multiple adaptive laws is systematically designed for the systems. Subsequently, a controller with only two adaptive laws is proposed to solve the problem of over parameterization. Under the designed controllers, all the signals in the closed-loop system are bounded-input bounded-output stable in probability, and the system output can almost surely track the target trajectory within a specified bounded error. Finally, simulation results are presented to show the effectiveness of the proposed approaches.