Event-triggered neural adaptive tracking control for output constrained nonlinear multi-agent systems with unmodeled dynamics and unknown signs

This paper studies the distributed cooperative control problem of uncertain non-strict feedback nonlinear multi-agent systems including time-varying output constraints, unmodeled state dynamics and unknown control directions. By introducing two universally constrained functions and using error coordinate transformation, the output constraints are effectively handled. Meanwhile, the convergence of cooperative tracking errors is also guaranteed. The dynamic signals that are observable and generated by the first-order auxiliary system are used to mitigate the effect of unmodeled state dynamics on the system. With the aid of the property of Gaussian function, the coupling problem among multi-agents and the non-strict feedback terms are well resolved. To tackle unknown control directions and save communication resources, a novel event-triggered neural adaptive cooperative control scheme is proposed via command filtered backstepping and dynamic surface control techniques without using Nussbaum functions. Through Lyapunov stability analysis, it is proved that all signals in the closed-loop system are semi-globally uniformly ultimately bounded and the Zeno behavior can be excluded. All followers’ outputs can track the leader's trajectory within the constraints. Finally, the simulation example also verifies that the proposed event-triggered control scheme is valid.