通过自适应神经固定时间控制,用于网络异质机器人系统的任务空间跟踪
Ren-Jie Gu1, Tao Han1, Bo Xiao1
1School of Electrical Engineering and Automation, Hubei Normal University, Huangshi 435005, PR China.
ISA transactions
|October 2, 2024
概括
本研究介绍了一种用于网络异质机器人系统 (NHRSs) 的新型神经网络 (NN) 控制方法. 它通过解决不确定性和提高趋同速度来确保固定时间跟踪.
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:
- 网络异质机器人系统 (NHRSs) 提出了复杂的控制挑战.
- 固定时间跟踪对于精确的机器人操作至关重要.
研究的目的:
- 为NHRSs开发基于神经网络 (NN) 的固定时间层次控制方法.
- 解决任务空间分布的自适应NN固定时间跟踪问题.
主要方法:
- 一种分层的控制策略,将问题分解为分布式估计和局部跟踪.
- 使用神经网络 (NN) 来近似复合不确定性.
- 设计一个自适应的神经固定时间本地跟踪控制器.
主要成果:
- 分布式估计器使追随者能够在固定的时间内获得领导者状态.
- 无线网络有效地接近未知的动态和干扰.
- 拟议的控制器确保追踪错误的固定时间融合,独立于初始状态.
结论:
- 基于NN的固定时间层次控制方法对NHRSs有效.
- 该方法通过对近似误差的新处理来提高稳定性和趋同速度.
- 实验结果验证了拟议的控制方法的优势.
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