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

This paper focuses on the leader-following consensus control problem for nonlinear multiagent systems subject to deferred asymmetric time-varying state constraints. A distributed event-triggered 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.