Neural-Network-Based Consensus Control for Multiagent Systems With Input Constraints: The Event-Triggered Case

In this paper, the neural-network (NN)-based consensus control problem is investigated for a class of discrete-time nonlinear multiagent systems (MASs) with a leader subject to input constraints. Relative measurements related to local tracking errors are collected via some smart sensors. A local nonquadratic cost function is first introduced to evaluate the control performance with input constraints. Then, in view of the relative measurements, an NN-based observer under the event-triggered mechanism is designed to reconstruct the dynamics of the local tracking errors, where the adopted event-triggered condition has a time-dependent threshold and the weight of NNs is updated via a new adaptive tuning law catering to the employed event-triggered mechanism. Furthermore, an ideal control policy is developed for the addressed consensus control problem while minimizing the prescribed local nonquadratic cost function. Moreover, an actor–critic NN scheme with online learning is employed to realize the obtained control policy, where the critic NN is a three-layer structure with powerful approximation capability. Through extensive mathematical analysis, the consensus condition is established for the underlying MAS, and the boundedness of the estimated errors is proven for actor and critic NN weights. In addition, the effect from the adopted event-triggered mechanism on the local cost is thoroughly discussed, and the upper bound of the corresponding increment is derived in comparison with time-triggered cases. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.