通过强化学习和机器人操纵器的集成滑动模式动量观察器来实现强大的近最佳PD-Like控制策略
IEEE transactions on cybernetics
|February 6, 2026
概括
一个新的强化学习 (RL) 控制方案增强了机器人操纵器的轨迹跟踪. 这种方法通过集成不确定性估计器和最佳跟踪控制器来提高效率和稳定性,以获得卓越的性能.
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
- 机器学习 机器学习
背景情况:
- 机器人操纵器需要精确的轨迹跟踪来完成复杂的任务.
- 现有的控制方法经常与不确定性和参数变化作斗争.
- 需要先进的控制策略来提高跟踪精度和系统稳定性.
研究的目的:
- 为机器人操纵器轨迹跟踪引入一种基于强化学习 (RL) 的新型控制方案.
- 通过结合不确定性估计器和最佳跟踪控制器来提高控制性能.
- 通过模拟和实验验证拟议的控制方案的有效性和可行性.
主要方法:
- 实现了带有整体滑动模式控制 (ISMC) 的动量观察器 (MO),用于不确定性估计.
- 使用比例衍生类 (PD类) 控制与基于演员关键神经网络 (NN) 的前控制相结合.
- 采用NN参数选择方案以提高效率并确保初始控制政策的可接受性.
- 应用利亚普诺夫函数分析来证明闭环系统的稳定性.
主要成果:
- 与传统的PD和FSTSMC方法相比,提议的基于RL的控制方案显示出优越的轨迹跟踪性能.
- 模拟和实验结果证实了新控制策略的有效性和可行性.
- 错误信号被证明是局限的,并汇聚到一个小的残余集,表明系统的稳定性.
结论:
- 基于RL的新型控制方案为机器人操纵器的轨迹跟踪提供了显著的改进.
- 不确定性估计和最佳控制的整合会提高性能和稳定性.
- 拟议的方法为先进的机器人控制应用提供了可行和有效的解决方案.
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