Distributed Optimization Control for High-Order Nonlinear Multiagent Systems: A Fully Distributed Event-Triggered Control Algorithm - Zhang, Jing; Yue, Huarong; Xia, Jianwei; Park, Ju H. (IEEE internet of things journal, 2025)
This article investigates the fully distributed optimization control problem for high-order nonlinear multiagent systems (MASs) with unknown nonlinearities. Initially, a fully distributed optimal signal estimator with adaptive gains is developed, which ensures that its output asymptotically converges to the optimal solution of the optimization problem without requiring any global information. Subsequently, the distributed optimization problem is transformed into a reference-tracking problem. By employing backstepping technique, a distributed control protocol is designed for each agent, enabling the outputs of all agents to asymptotically track the estimator's output. It is shown that the boundedness of all signals in closed-loop systems and the distributed optimization objective can be successfully achieved. Furthermore, to reduce continuous communication among agents, the optimal signal estimator is redesigned by introducing a dynamic event-triggered mechanism (DETM), making it not only a fully distributed algorithm but also one that minimizes energy consumption. Finally, the effectiveness of the algorithm is validated through numerical and practical simulations.