固定时间反复的NN学习控制不确定的机器人操纵器与时间变化的约束:实验验证
Qingxin Shi1, Changsheng Li1, Rui He1
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了一种用于机器人操纵器的新型神经学习控制器,确保固定时间的融合和输出约束. 它有效地处理未知的动态和干扰,使用循环神经网络 (RNN).
科学领域:
- 机器人和控制系统 机器人和控制系统
- 机器学习用于控制控制.
- 自动化中的人工智能
背景情况:
- 机器人操纵器需要精确的动态跟踪来完成复杂的任务.
- 传统的控制方法与未知的动态和外部干扰作斗争.
- 现有的方法往往缺乏对固定时间收和输出约束的保证.
研究的目的:
- 为机器人操纵器开发一个学习控制框架.
- 在输出约束下实现追踪错误的固定时间收.
- 通过在线学习来解决未知的操纵器动态和外部干扰.
主要方法:
- 提出了一个动态表面控制 (DSC) 框架,集成一个屏障Lyapunov函数 (BLF) 和一个循环神经网络 (RNN).
- 引入了可变时间的触角型BLF,用于固定时间的虚拟控制器设计.
- 采用基于RNN的在线近似器来弥补未知的系统动态和外部干扰.
主要成果:
- 保证追踪错误的固定时间趋同到源头周围的小社区.
- 确保操纵器的实际轨迹保持在规定的输出约束范围内.
- 通过实验结果证明了更好的跟踪精度和有效的在线估计未知的动态.
结论:
- 拟议的固定时间,输出受限制的神经学习控制器有效地提高了机器人操纵器跟踪性能.
- BLF和RNN的集成为未知的动态和干扰提供了强大的解决方案.
- 该框架为高精度机器人控制应用提供了一个有前途的方法.
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