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Distributed adaptive neural consensus tracking control of MIMO stochastic nonlinear multiagent systems with actuator failures and unknown dead zones
Author(s) -
Wu Zumin,
Wu Yuefei,
Yue Dong
Publication year - 2018
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2940
Subject(s) - control theory (sociology) , multi agent system , consensus , nonlinear system , synchronization (alternating current) , controller (irrigation) , computer science , tracking error , bounded function , actuator , adaptive control , artificial neural network , robust control , control (management) , artificial intelligence , mathematics , channel (broadcasting) , computer network , mathematical analysis , physics , quantum mechanics , agronomy , biology
Summary This paper focuses on the leader‐following consensus control problem of stochastic multiagent systems with unknown input dead‐zone nonlinearity, unknown actuator failures, and nonlinear dynamics. A distributed adaptive neural consensus tracking controller is presented for stochastic multiagent systems directions under directed graphs, which can achieve predefined synchronization error bounds. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multiswitching robust neuroadaptive controller in the neural approximation domain can achieve globally uniformly ultimately bounded tracking stability of multiagent systems recently. By constructing a smooth dead‐zone inverse and applying the dynamic surface control technique, distributed consensus controllers are developed to guarantee that the trajectories of synchronization error converge to a small neighborhood of the origin. Two simulation examples are provided to illustrate the effectiveness and advantage of the proposed control scheme.