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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >3-D Learning-Enhanced Adaptive ILC for Iteration-Varying Formation Tasks
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3-D Learning-Enhanced Adaptive ILC for Iteration-Varying Formation Tasks

机译：迭代变化编队任务的3D学习增强型自适应ILC

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摘要

This paper explores the formation control problem of repetitive nonlinear homogeneous and asynchronous multiagent networks, where the early starting agent is designated as the parent, and the later starting agent with a small delayed time is designated as the child. Moreover, the desired formation reference is allowed to be different from iteration to iteration. A space-dimensional dynamic linearization method is presented to build the linear dynamic relationship between two parent-child agents in a networked system. Then, a 3-D learning-enhanced adaptive iterative learning control (3D-AILC) is proposed by utilizing the additional control information from previous time instants, iterative operations, and parent agents. In other words, the proposed method processes 3-D dynamics to strengthen its learnability, i.e., time dimension, iteration dimension, and space dimension. The desired formation signal is incorporated into the learning control law to compensate its iterative variation to achieve a fast and precise tracking performance. The proposed 3D-AILC is data based and does not use an explicit mechanistic model. The validity of the proposed approach is proven theoretically and tested through simulations as well. Moreover, the proposed method also works well with time-iteration-varying topologies and nonrepetitive uncertainties.
机译：本文探讨了重复的非线性齐次和异步多主体网络的构造控制问题，其中早期启动主体被指定为父代，而延迟时间较小的后续初始主体被指定为子代。此外，允许所需的地层参考在每次迭代中都不同。提出了一种空间维动态线性化方法，以建立网络系统中两个父子代理之间的线性动态关系。然后，通过利用来自先前时刻，迭代操作和父代代理的附加控制信息，提出了一种3D学习增强型自适应迭代学习控制（3D-AILC）。换句话说，提出的方法处理3-D动力学以增强其可学习性，即时间维，迭代维和空间维。所需的编队信号被合并到学习控制定律中，以补偿其迭代变化，以实现快速精确的跟踪性能。提出的3D-AILC是基于数据的，没有使用显式的机械模型。理论上证明了该方法的有效性，并通过仿真进行了测试。此外，所提出的方法还适用于时变迭代拓扑和非重复不确定性。

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