Event-Triggered Cooperative Learning from Adaptive Neural Control of Nonlinear Systems with Output Constraints

This paper uses the event-triggered strategy to address the cooperative learning control for a group of nonlinear multiagent systems with output constraints. During control process, radial basis function neural network (RBF NN) approximation of the unknown function is obtained along the union of all system trajectories. By introducing the event-triggered strategy, each agent can intermittently broadcast its NNs weight estimation to its neighboring agents. Meanwhile, a strict interevent intervals is guaranteed to avoid the Zeno behaviour. Furthermore, the tracking problem is addressed, and the time-varying output constraints are never violated by introducing time-varying barrier Lyapunov functions (BLF). Finally, we give a simulation example to verify the effectiveness of the control schemes proposed in this paper.