适应性非单元快速终端滑动模式轨迹跟踪控制机器人操纵器的新配置基于TD3深度强化学习和非线性干扰观察者
Huaqiang You1, Yanjun Liu1,2, Zhenjie Shi1
1School of Mechanical Engineering, State Key Laboratory of Advanced Equipment and Technology for Metal Forming, Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of Ministry of Education, National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究介绍了一种非单元快速终端滑动模式控制 (NFTSMC) 策略,增强了双延迟深确定性政策梯度 (TD3) 和非线性干扰观察器 (NDO). TD3-NFTSMC方法显著提高了机器人操纵器轨迹跟踪精度和对抗干扰的稳定性.
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:
- 机器人操纵器面临来自建模错误,外部干扰和摩擦的挑战,影响轨迹跟踪的准确性.
- 传统的控制方法经常遭受奇点问题和喋喋不休,限制性能.
- 需要先进的控制策略来提高复杂的机器人应用中的精度和稳定性.
研究的目的:
- 开发和验证一种新的机器人操纵器控制策略,克服现有方法的局限性.
- 为了提高轨迹跟踪的准确性和稳定性,防止建模错误和干扰.
- 为了利用深度强化学习进行自适应控制增益调整.
主要方法:
- 一个5DOF模块化连续机器人操纵器的设计,具有已建立的动力学和动态模型.
- 实施非线性干扰观察员 (NDO) 用于干扰估计和前补偿.
- 开发了一种改进的非单元快速终端滑动模式控制 (NFTSMC),采用边界层技术.
- 训练双延迟深度决定性政策梯度 (TD3) 代理用于NFTSMC控制器的自适应增益调整.
- 使用利亚普诺夫理论进行稳定性分析.
主要成果:
- 与NDONFT相比,提议的TD3-NFTSMC算法显著减少了三个关节的平均绝对位置跟踪误差7.14%,19.94%和6.14%.
- 三个关节的平均绝对速度跟踪误差减少了1.78%,9.10%和2.11%.
- 控制系统表现出强大的稳定性,可以应对突然的,未知的时间变化的干扰.
结论:
- 集成的TD3-NFTSMC战略有效地提高了机器人操纵器的轨迹跟踪精度.
- 该方法在具有挑战性的条件下,与现有的控制方法相比,提供了更高的性能和稳定性.
- 该研究验证了深度强化学习在复杂机器人系统的自适应控制中的潜力.
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