基于强化学习的PLC控制技术在电气自动化设备中的应用

Application of Reinforcement Learning-Based PLC Control Technology in Electrical Automation Equipment

针对电气自动化设备控制中面临的动态性和复杂性问题,提出了自适应PLC控制算法和分布式PLC控制架构这2种基于强化学习的PLC控制技术。自适应PLC控制算法通过引入强化学习机制自主优化控制策略,以提高控制精度和响应速度。分布式PLC控制架构采用多智能体协同学习的方式,实现了大规模设备群的分布式优化控制,有效降低了通信负载和计算复杂度。仿真实验结果表明,2种技术均取得了优于传统控制方法的性能表现,验证了将强化学习引入PLC控制领域的可行性和有效性。

This paper addresses the dynamic and complex issues faced in the control of electrical automation equipment by proposing two types of reinforcement learning-based PLC control technologies,namely,an adaptive PLC control algorithm and a distributed PLC control architecture.The adaptive PLC control algorithm autonomously optimizes control strategies by introducing a reinforcement learning mechanism,thereby improving control accuracy and response speed.The distributed PLC control architecture employs a multi-agent collaborative learning approach to achieve distributed optimal control of large-scale equipment groups,effectively reducing communication load and computational complexity.The results of simulation experiments demonstrate that both technologies have outperformed traditional control methods,confirming the feasibility and effectiveness of introducing reinforcement learning into the field of PLC control.