Application of Reinforcement Learning-Based PLC Control Technology in Electrical Automation Equipment
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.
万方数据
维普
