基于扩展模糊状态观测的学习预测控制,用于跟踪不确定的操纵器的轨迹
1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
ISA transactions
|November 29, 2025
概括
本研究介绍了一种基于学习的预测控制方法,使用模糊扩展状态观察器 (LPC-FESO) 进行机器人轨迹跟踪. 这种新的方法改善了强化学习 (RL) 在不确定的机器人系统中的融合和干扰排斥.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 机器人操纵器是复杂的多输入,多输出系统,具有固有的非线性和合.
- 有效的轨迹跟踪对于机器人任务至关重要,但受到诸如干扰和未建模的动态等不确定性的挑战.
研究的目的:
- 开发一种基于学习的强有力的预测控制方法,用于在不确定的机器人操纵器中准确的轨迹跟踪.
- 为了解决在随机环境中强化学习 (RL) 的缓慢融合问题.
主要方法:
- 提出了一个基于学习的预测控制与模糊的扩展状态观察者 (LPC-FESO).
- 集成的非线性预测控制与深度决定性政策梯度 (DDPG),使用模糊的后退方法进行初始控制.
- 设计了一个模糊的扩展状态观察器 (FESO) 来增强干扰拒绝和平衡系统状态.
主要成果:
- LPC-FESO框架在干扰和状态约束下证明了理论上的收特性.
- 在2-DOF操纵器上的模拟显示了有效的轨迹跟踪位置和速度.
- 该方法表现出强大的干扰排斥能力,并符合性能标准.
结论:
- 拟议的LPC-FESO方法为不确定的机器人操纵器的轨迹跟踪提供了一个高效和强大的解决方案.
- 模糊逻辑和强化学习的整合提高了控制性能和系统稳定性.
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