基于强化学习算法的自适应PI控制器,用于DC电机的速度控制.
Ulbio Alejandro-Sanjines1, Anthony Maisincho-Jivaja1, Victor Asanza2
1Escuela Superior Politécnica del Litoral, Guayaquil 090903, Ecuador.
Biomimetics (Basel, Switzerland)
|September 27, 2023
概括
本研究介绍了一种可适应的PID控制器,用于使用强化学习控制直流电机的转速. 这种新的方法可以自动优化控制器的效益,提高自动化工业流程的效率.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自动化工业流程依赖控制器来准确生成输出信号.
- 经典的PID控制器在系统条件发生变化时需要重新调节,从而影响生产.
- 现有的控制方法面临着动态或不断变化的系统参数的挑战.
研究的目的:
- 开发适应性PID控制器用于直流电机转速控制.
- 利用人工智能,特别是强化学习,用于自主控制器调.
- 消除在控制器增益确定中需要先前的系统知识的需要.
主要方法:
- 实现了一个适应性PID控制器用于直流电机转速装置.
- 采用了强化学习算法与一个演员-关键代理.
- 使用了深度决定性政策梯度与双延迟 (DDPG TD3) 算法.
- 配置了一个由300个神经元组成的神经网络,用于代理学习.
主要成果:
- 适应式PID控制器在没有系统识别的情况下成功生成了适当的收益.
- DDPG的TD3算法促进了政策优化和批评者培训.
- 性能比较证明了开发的控制器与经典方法相比的有效性.
结论:
- 开发的自适应PID控制器为动态环境中的直流电机转速控制提供了强大的解决方案.
- 强化学习,特别是DDPG TD3,为自动控制器调整提供了有效的机制.
- 这种人工智能驱动的方法通过消除手动调整要求来提高工业过程效率.
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