基于有限时间的融合,对跟踪移动机器人进行强有力的控制,将神经网络归零
Yuxuan Cao1, Boyun Liu1, Jinyun Pu1
1College of Power Engineering, Naval University of Engineering, Wuhan, China.
Frontiers in neurorobotics
|October 6, 2023
概括
这项研究引入了一种新的有限时间收归零神经网络,用于在非线性跟踪移动机器人中精确的轨迹跟踪. 该方法表现出卓越的性能和稳定性,即使在受干扰的环境中.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 追踪移动机器人是复杂的非线性系统,对准确的轨迹跟踪提出了重大挑战.
- 现有的控制方法经常与固有的非线性动态和影响这些机器人的潜在干扰作斗争.
研究的目的:
- 开发和验证一种新的神经网络控制策略,以增强可追踪移动机器人的轨迹跟踪.
- 设计一个强大的控制系统,能够处理非线性动态和外部干扰.
主要方法:
- 一个新的分数指数激活函数被设计为一个归零的神经网络.
- 开发了一种隐式衍生动态模型,称为有限时间收归零神经网络.
- 利亚普诺夫稳定理论被用于模型分析,确定收时间界限和调查稳定性.
主要成果:
- 在数值实验中,成功追踪了八个形状路径的轨迹.
- 拟议的有限时间收,将神经网络归零,与现有方法相比,证明了有效性和优越性.
- 在各种错误干扰条件下验证了模型的稳定性.
结论:
- 有限时间收归零神经网络为跟踪移动机器人的轨迹跟踪问题提供了有效的解决方案.
- 拟议的模型提供了增强的性能和稳定性,使其适合于现实世界的应用,包括扰乱的环境.
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