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Optimal Time-Varying Containment Control for Nonlinear Multiagent Systems With Predefined-Time Stability
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DOI：10.1109/tcns.2026.3672044.png)
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Abstract
En 中文
Containment control of multiagent systems (MASs) has found widespread applications in various scenarios such as search-and-rescue operations, escort missions, and so on. In this article, we propose a novel distributed control algorithm to achieve time-varying containment control of nonlinear MASs, which not only guarantees the convergence of followers to the convex hull spanned by dynamic leaders within a predefined time but also optimizes system performance under nonlinear dynamics. Moreover, the issue of full-state constraints is properly addressed by proposing a universal nonlinear transformation, which integrates constraints and unconstrained cases into a unified framework. Specifically, the reinforcement learning strategy with the identifier–actor–critic structure is incorporated into the design process to complete containment tasks quickly and optimize resource utilization. Through rigorous proof, the upper boundedness of developed actor–critic adaptive laws is verified, which is an essential process to verify the practical predefined-time stability of the closed-loop system. Finally, both simulation studies and real-world experiments on unmanned surface vehicles are carried out to verify the effectiveness of the proposed scheme.
Keywords:
Agents and autonomous systems
containment control
predefined time
reinforcement learning (RL)
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