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Event-Triggered Adaptive Neural Control for Multiagent Systems with Deferred State Constraints

YANG Bin, CAO Liang, XIAO Wenbin, YAO Deyin, LU Renquan   

  1. School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2020-08-29 Revised:2021-01-26 Published:2022-06-20
  • Supported by:
    This research was partially supported by the Science Center Program of the National Natural Science Foundation of China under Grant No. 62188101, and the Major Program of the National Natural Science Foundation of China under Grant Nos. 61690210 and 61690212, the National Natural Science Foundation of China under Grant Nos. 62103164 and 61703437.

YANG Bin, CAO Liang, XIAO Wenbin, YAO Deyin, LU Renquan. Event-Triggered Adaptive Neural Control for Multiagent Systems with Deferred State Constraints[J]. Journal of Systems Science and Complexity, 2022, 35(3): 973-992.

This paper focuses on the leader-following consensus control problem for nonlinear multiagent systems subject to deferred asymmetric time-varying state constraints. A distributed eventtriggered adaptive neural control approach is advanced. By virtue of a distributed sliding-mode estimator, the leader-following consensus control problem is converted into multiple simplified tracking control problems. Afterwards, a shifting function is utilized to transform the error variables such that the initial tracking condition can be totally unknown and the state constraints can be imposed at a specified time instant. Meanwhile, the deferred asymmetric time-varying full state constraints are addressed by a class of asymmetric barrier Lyapunov function. In order to reduce the burden of communication, a relative threshold event-triggered mechanism is incorporated into controller and Zeno behavior is excluded. Based on Lyapunov stability theorem, all closed-loop signals are proved to be semi-globally uniformly ultimately bounded. Finally, a practical simulation example is given to verify the presented control scheme.
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