复杂的动态轨迹的无模型跟踪控制与机器学习
Zheng-Meng Zhai1, Mohammadamin Moradi1, Ling-Wei Kong1
1School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, 85287, USA.
Nature communications
|September 14, 2023
概括
本研究介绍了用于机器人操纵器控制的无模型机器学习框架,使系统能够使用储库计算和部分观察到的状态来遵循所需的轨迹,即使有噪音和不确定性.
科学领域:
- 机器人和控制工程 机器人和控制工程
- 机器学习应用 机器学习应用
- 动态系统理论 动态系统理论
背景情况:
- 非线性跟踪控制对于机器人技术至关重要,但传统方法需要完整的系统模型知识.
- 设计控制器通常需要完整的状态信息,这在现实应用中并不总是可用.
研究的目的:
- 开发一种无模型的机器学习框架,用于控制带有部分观察状态的机器人操纵器.
- 实施一种新的控制策略,使用储库计算来增强追踪能力.
主要方法:
- 为双臂机器人操纵器开发了一个无模型的机器学习框架.
- 储计算被用作控制器,利用部分观察到的状态.
- 随机输入用于训练,观察到的状态和它们的直接未来作为输入组件.
主要成果:
- 该框架展示了有效的控制,用于跟踪各种周期性和混乱信号.
- 控制系统表现出对测量噪声,干扰和系统不确定性的强度.
- 无模型方法成功地控制了机器人操纵器,仅使用部分状态观测.
结论:
- 开发的无模型机器学习框架为机器人操纵器提供了传统基于模型的控制的可行替代方案.
- 储水器计算提供了一种有效的方法,可以使用有限的状态信息来实现跟踪控制.
- 这种方法提高了机器人系统在复杂和不确定的环境中的适用性.
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