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Adaptive fuzzy iterative learning control with initial-state learning for coordination control of leader-following multi-agent systems

机译：具有初始状态学习的自适应模糊迭代学习控制，用于领导者跟随多主体系统的协调控制

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摘要

We propose a distributed adaptive fuzzy iterative learning control (ILC) algorithm to deal with coordination control problems in leader-following multi-agent systems in which each follower agent has unknown dynamics and a non-repeatable input disturbance. The ILC protocols are designed with distributed initial-state learning and it is not necessary to fix the initial value at the beginning of each iteration. A fuzzy logical system is used to approximate the nonlinearity of each follower agent. A fuzzy learning component is an important learning tool in the protocol, and combined time-domain and iteration-domain adaptive laws are used to tune the controller parameters. The protocol guarantees that the follower agents track the leader for the consensus problem and keep at a desired distance from the leader for the formation problem on [0, T]. Simulation examples illustrate the effectiveness of the proposed scheme.
机译：我们提出了一种分布式自适应模糊迭代学习控制（ILC）算法来解决领导者跟随多主体系统中的协调控制问题，其中每个跟随者主体具有未知的动力学特性并且具有不可重复的输入干扰。 ILC协议设计为具有分布式初始状态学习，因此不必在每次迭代的开始时就固定初始值。模糊逻辑系统用于近似每个跟随者代理的非线性。模糊学习组件是该协议中的重要学习工具，并且结合时域和迭代域自适应律来调整控制器参数。该协议保证追随者代理跟踪领导者的共识问题，并在[0，T]上与领导者保持理想距离，以应对编队问题。仿真算例说明了该方案的有效性。

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