Data-Driven Fuzzy Group Formation–Containment Control of Nonlinear Multiagent Systems With Asymmetric Input Saturation

作者：Chuanjian Li, Xiaoping Wang, Zhigang Zeng, Xiaofeng Zong · 发表于：IEEE Transactions on Fuzzy Systems · 年份：2025 · DOI：10.1109/tfuzz.2025.3643610 · 被引用次数：3 · 研究领域：Distributed Control Multi-Agent Systems、Adaptive Dynamic Programming Control、Reinforcement Learning in Robotics

The existing group formation-containment (GFC) studies for multi-agent systems (MASs) depend on system model information and generally neglect input saturation constraint, thereby limiting their applicability to MASs with unknown system model and input saturation. This paper investigates the data-driven GFC control problem of nonlinear MASs with asymmetric input saturation. First, a novel communication topology selection algorithm with relaxed topology conditions is proposed. Then, to tackle the challenge posed by asymmetric input saturation, a novel nonquadratic performance index function with a simplified formulation is designed and the corresponding Hamilton-Jacobi-Bellman equation is derived. On this basis, an effective value iteration algorithm is proposed to determine the optimal GFC control policy, accompanied by the rigorous mathematical analysis. By establishing the critic-actor framework based on generalized fuzzy hyperbolic model, a novel data-driven algorithm is proposed to achieve GFC under asymmetric input saturation, which overcomes the dependence on system model. Finally, some simulation results are provided to verify the effectiveness and superiority of the proposed data-driven GFC algorithm.