Cooperative Tracking Control for Nonlinear MASs Under Event-Triggered Communication

The neural network-based adaptive backstepping method is an effective tool to solve the cooperative tracking problem for nonlinear multiagent systems (MASs). However, this method cannot be directly extended to the case without continuous communication. It is because the discontinuous communication results in discontinuous signals in this case, the standard backstepping method is inapplicable. To solve this problem, a hierarchical design scheme that involves distributed cooperative estimators and neural network-based decentralized tracking controllers is proposed. By introducing a dynamic event-triggered mechanism, cooperative intermediate parameter estimators are first designed to estimate the unknown parameters of the leader. By using the interpolation polynomial method, these estimators are extended to smooth estimators with high-order derivatives to guarantee that the backstepping method is applicable. Based on the state of the smooth estimators, a backstepping-based decentralized neural network tracking controller is designed. It is shown that the tracking errors are asymptotically convergent and all the signals in the closed-loop systems are bounded. Compared with the existing cooperative tracking results for nonlinear MASs with event-triggered communication, a more general class of MASs is considered in this article and a better performance in terms of asymptotic tracking is achieved. Finally, a simulation example is given to show the effectiveness of our developed method.

Automated summary

The neural network-based adaptive backstepping method is an effective tool for solving the cooperative tracking problem in nonlinear multiagent systems (MASs). However, it cannot be directly applied to cases without continuous communication due to discontinuous signals caused by the absence of continuous communication. To address this issue, a hierarchical design scheme involving distributed cooperative estimators and neural network-based decentralized tracking controllers is proposed. The proposed method uses dynamic event-triggered mechanism to estimate unknown parameters and design a backstepping-based decentralized neural network tracking controller, achieving asymptotic tracking and bounded signals in the closed-loop systems.