使用三层神经网络对非线性多元代理进行强大的自适应领导跟随形成控制
IEEE transactions on cybernetics
|February 6, 2024
概括
本研究引入了一种新型神经网络 (NN) 方法来控制多剂形成,确保复杂系统的稳定性和准确跟踪. 该方法有效地处理异质代理组中未知的非线性和干扰.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 人工智能的人工智能
背景情况:
- 形成控制对于多代理系统至关重要.
- 异质,非线性和不确定的代理物带来了重要的控制挑战.
- 现有的神经网络 (NN) 设计往往缺乏预先定义的层结构.
研究的目的:
- 解决对异质,非线性,不确定的二阶剂的形成控制问题.
- 呈现一个可调的,三层的NNN,能够近似未知的非线性.
- 在多代理系统中确保稳定性和半全球性非对称跟踪.
主要方法:
- 一个可调整的三层神经网络 (NN) 是为非线性近似设计的.
- 利亚普诺夫理论被用来推导NN重量调定律.
- 使用一种控制策略,结合了错误反信号的强大整体和基于NN的控制.
主要成果:
- 拟议的NN设计允许预设每层神经元的数量.
- 控制策略有效地弥补了未知的动态和干扰.
- 利亚普诺夫稳定理论证明了该系统的稳定性和半全球的非对称跟踪.
结论:
- 开发的基于NN的形成控制方法对异质的多剂系统有效和高效.
- 这种方法在NN设计中提供了优势,因为它避免了层配置的试错.
- 严格的稳定性分析证实了拟议的控制战略的可靠性.
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