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Open-loop and closed-loop Dα-type iterative learning control for fractional-order linear multi-agent systems with state-delays

机译:具有状态延迟的分数级线性多智能体系的开环和闭环Dα型迭代学习控制

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摘要

This study focuses on implementing consensus tracking using both open-loop and closed-loop D-alpha-type iterative learning control (ILC) schemes, for fractional-order multi-agent systems (FOMASs) with state-delays. The desired trajectory is constructed by introducing a virtual leader, and the fixed communication topology is considered and only a subset of followers can access the desired trajectory. For each control scheme, one controller is designed for one agent individually. According to the tracking error between the agent and the virtual leader, and the tracking errors between the agent and neighboring agents during the last iteration (for open-loop scheme) or the current running (for closed-loop scheme), each controller continuously corrects the last control law by a combination of communication weights in the topology to obtain the ideal control law. Through the rigorous analysis, sufficient conditions for both control schemes are established to ensure that all agents can achieve the asymptotically consistent output along the iteration axis within a finite-time interval. Sufficient numerical simulation results demonstrate the effectiveness of the control schemes, and provide some meaningful comparison results.
机译:本研究侧重于使用开环和闭环D-Alpha型迭代学习控制(ILC)方案来实施共识跟踪,用于具有状态延迟的分数级多智能体系(FOMASS)。通过引入虚拟领导者构建所需的轨迹,并且考虑固定通信拓扑,并且仅追随者的子集可以访问所需的轨迹。对于每个控制方案,一个控制器是单独为一个代理设计的。根据代理和虚拟领导者之间的跟踪误差,以及在最后一次迭代期间代理和相邻代理之间的跟踪错误(用于开环方案)或当前运行(对于闭环方案),每个控制器都不断纠正最后的控制法通过拓扑中的通信权重的组合来获得理想的控制法。通过严格的分析,建立了对两个控制方案的充分条件,以确保所有代理可以在有限时间间隔内沿迭代轴达到渐近一致的输出。足够的数值模拟结果证明了控制方案的有效性,并提供了一些有意义的比较结果。

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