机器人操纵器的自适应预定义时间跟踪控制基于演员-关键强化学习
Yong Qin1, Yuan Sun2, Jun Huang2
1School of Artificial Intelligence and Smart Manufacturing, Hechi University, Hechi 546300, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究引入了一种新的预定义时间适应神经控制,用于使用Actor-Critic强化学习的不确定操纵者. 它确保了操纵器跟踪控制的快速,有保证的融合,优于PID方法.
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:
- 操纵系统经常面临动态的不确定性,使精确的控制变得复杂.
- 传统的控制方法可能会在复杂系统的快速融合和保证的结算时间方面扎.
研究的目的:
- 为不确定的操纵系统开发一种新的预定义时间的自适应神经跟踪控制方法.
- 将预定义时间稳定理论与强化学习相结合,以提高控制性能.
- 为了实现快速融合,明确预设结算时间限制.
主要方法:
- 使用神经网络的Actor-Critic强化学习框架.
- 一个Actor网络接近未知的动态,并产生控制信号.
- 关键网络通过评估成本-to-go函数来优化学习过程.
- 将特定术语纳入控制法律和重量更新,以实现预定义时间的融合.
- 运用利亚普诺夫稳定理论进行严格的分析.
主要成果:
- 在预设的边界内证明了跟踪错误的预定义时间收.
- 确保所有闭环信号的边界性.
- 沉时间可通过单个设计参数进行调节,独立于初始条件.
- 与传统的PID控制相比,模拟结果显示性能优越.
结论:
- 提出的预定义时间的自适应神经控制方法对不确定的操纵系统是有效的.
- 演员-批评强化学习框架成功实现了快速和稳定的跟踪控制.
- 这种方法对机器人操纵器的现有控制策略提供了显著的改进.
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