灵活机器人的双时间尺度跟踪控制,采用Primal-Dual反向增强学习
概括
这项研究引入了一种新的两次尺度初级-双重反向增强学习 (PD-IRL) 框架,用于灵活的机器人 (FRs),通过平衡速度和抑制振动来改善不完整信号的跟踪控制.
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:
- 灵活的机器人 (FR) 需要轻量化设计以快速移动,但这可能导致振动和建模错误,影响跟踪控制.
- 不完整的参考信号对FRs的精确控制构成重大挑战.
研究的目的:
- 为FRs开发一个强大的跟踪控制框架,可以处理不完整的参考信号.
- 通过模仿学习,在FR中平衡跟踪速度与抑制振动.
- 确保稳定和最佳的政策获取,尽管有非凸的约束.
主要方法:
- 提出了一个二次尺度的初级-双重反向强化学习 (PD-IRL) 框架.
- 可接受的策略被视为稳定运行的非凸输入约束.
- FR模仿专家的演示,包括刚性和灵活的动作.
- 非凸的优化问题被转化为全球最佳政策的双重问题.
- 多个线性独立的路径用于状态空间探索,以增强融合.
主要成果:
- 拟议的PD-IRL框架有效地使FRs能够使用不完整的参考信号执行跟踪任务.
- 该方法通过模仿专家行为来实现跟踪速度和抑制振动之间的平衡.
- 全球最佳政策是通过将非凸的优化问题转化为它们的双重形式来获得的.
- 模拟证明了与现有方法相比,开发的框架的有效性和优势.
结论:
- 双时间尺度PD-IRL框架为具有不完整参考信号的灵活机器人中的跟踪控制提供了有效的解决方案.
- 该方法成功地平衡了速度和振动等性能指标,确保了稳定的运行.
- 严格的分析证实了拟议方法的趋同和稳定性,强调了其实际适用性.
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