非线性多智能体系统的自适应模糊事件触发包容控制

Adaptive fuzzy event-triggered containment control for nonlinear multi-agent systems

摘要:

针对具有输入延迟和随机干扰的非线性多智能体系统，提出了一种基于规定性能和事件触发机制的自适应模糊控制方法；该方法采用了基于相对阈值的事件触发机制，从而降低了多智能体系统的硬件要求，并最大限度地减少了智能体之间的通信。与大多数事件触发机制相比，这种设计不仅简单方便，而且有效减少了控制过程中的网络通信负荷；模糊逻辑系统和Pade近似法分别用于处理多智能体系统中的未知非线性函数和输入延迟；为进一步提高系统的瞬态和稳态性能，设计了一种新的自适应模糊包容控制器，该控制器加入了规定的性能函数，确保包容误差收敛到预定的性能标准；此外，为了降低计算复杂度，还引入了指令滤波技术；基于 Lyapunov 稳定性理论，证明了闭环系统的稳定性，只存在较小的包容误差，并避免了芝诺现象；通过理论分析和仿真实验，进一步验证了所提控制方法的有效性。

Abstract:

An adaptive fuzzy control method based on prescribed performance and event-triggered mechanism is proposed for nonlinear multi-intelligent body systems with input delays and random disturbances; the method employs an event-triggered mechanism based on relative thresholds, which reduces the hardware requirements of the multi-agent system and minimizes the communication between agents. Compared with most event-triggered mechanisms, this design is not only simple and convenient, but also effectively reduces the network communication load during the control process; the fuzzy logic system and the Pade approximation are used to deal with unknown nonlinear functions and input delays, respectively, in the multi-agent system; in order to further improve the system's transient and steady-state performances, a new adaptive fuzzy containment controller is designed, which incorporates a prescribed performance function to ensure that the containment error converges to a predefined performance criterion; in addition, in order to reduce the computational complexity, the command filtering technique is introduced; based on the Lyapunov stability theory, the stability of the closed-loop system is demonstrated with only a small inclusion error and the Zeno phenomenon is avoided; the effectiveness of the proposed control method is further verified through theoretical analysis and simulation experiments.