Practical Prescribed-Time Bipartite Time-Varying Formation Control for Multiagent Systems With Adaptive Self-Triggered

This paper investigates the problem of practical prescribed-time fuzzy bipartite time-varying formation control for nonlinear multiagent systems. To achieve practical prescribed-time control, a bounded and continuous prescribed-time evolution function is constructed, and a new stability lemma is derived, proving that the proposed controller avoids singularities and ensures synchronization errors converge to a neighborhood independent of initial conditions within the prescribed time. Considering cooperative-competitive interactions among the agents, a fuzzy bipartite time-varying formation controller is designed to achieve formation control within a specified time while keeping all closed-loop signals bounded. Furthermore, to reduce communication costs, an adaptive self-triggered mechanism is introduced. This mechanism maps the prescribed time to a dynamic triggering function, enabling the controller to adjust triggering conditions based on the user-defined convergence time, thereby balancing speed and communication costs. Finally, the effectiveness of the proposed method is validated through a simulation case involving five uncrewed ground vehicles.Note to Practitioners—This study addresses the formation control problem in nonlinear multiagent systems and proposes a practical prescribed-time bipartite formation control strategy, particularly suited for complex cooperative and competitive relationships. Traditional methods often face issues related to communication overhead and dynamic uncertainties, while the proposed approach significantly optimizes system performance by introducing a prescribed-time evolution function and an adaptive self-triggered mechanism, particularly in scenarios with limited communication resources. The self-triggered mechanism enables the system to adaptively adjust the triggering conditions based on the user-defined convergence time, thereby ensuring rapid convergence while reducing unnecessary communication. This approach is particularly applicable to scenarios that require fast responses and low communication overhead, offering broad industrial application potential.