基于双延迟深定论政策梯度RL的自适应控制器的设计,用于调节直流电机的转速,考虑到不确定性
P Gaur1, V P Singh1, T Varshney2
1Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, 302017, India.
Scientific reports
|January 9, 2026
概括
本研究介绍了用于DC电机转速调节的双延迟深度决定性 (TD3) 政策梯度增强学习控制器. 与基准控制器相比,它在应对环境不确定性方面表现出卓越的适应性和稳定性.
科学领域:
- 控制系统工程 控制系统工程
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 强化学习 (RL) 在优化复杂决策方面表现出色.
- 在许多工业应用中,直流电机的转速控制是至关重要的.
- 环境的不确定性挑战了传统的控制方法.
研究的目的:
- 开发和评估基于TD3 RL的适应性变速控制器,用于直流电机.
- 将TD3控制器的性能与基准控制器进行比较.
- 评估控制器在动态环境条件下的强度和适应性.
主要方法:
- 实施双延迟深度决定性 (TD3) 政策梯度RL算法.
- 为直流电机模型开发适应性变速控制器.
- 使用基准控制器技术进行比较分析.
- 通过模拟使用恒定和变速设定点进行性能评估.
主要成果:
- TD3控制器表现出增强的适应性和稳定性.
- 使用错误指数 (ISE,ITAE,IAE,ITSE) 的定量分析证实了优异的性能.
- 控制器有效地减少了动态操作条件下的错误.
结论:
- TD3 RL为直流电机的转速控制提供了一个高效和适应性的解决方案.
- 拟议的控制器在不确定的环境中比传统方法具有显著的优势.
- 这种方法为直流电机提供了精确的转速调节和误差最小化.
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