Event-/Self-Triggered Adaptive Optimal Consensus Control for Nonlinear Multiagent System With Unknown Dynamics and Disturbances
Qinglai Wei, Hao Jiang
Abstract
In this article, the optimal consensus tracking control for nonlinear multiagent systems (MASs) with unknown dynamics and disturbances is investigated via adaptive dynamic programming (ADP) technology. Taking into account the disturbance as control inputs, the optimal control problem for the nonlinear MASs is reformulated as a multiplayer zero-sum differential game. In addition, a single network ADP structure is constructed to approach the optimal consensus control policies. Subsequently, an event triggering mechanism is implemented to reduce the workload of the controller and conserve computing and communication resources. Since then, in order to further streamline the intricacies of controller design, this work is extended to self-triggered cases to alleviate the need for hardware devices to continuously monitor signals. By using the Lyapunov method, the stability of the nonlinear MASs and the uniform ultimate boundedness (UUB) of the weight estimation error of the critic neural network (NN) is proved. Finally, the simulation results for an MAS consisting of a single-link robot validate the effectiveness of the proposed control method.