In this article, the method of dynamic performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes is proposed. This method combines the digital twin model of the PID control process based on system identification and adaptive deep learning and the PID tuning intelligent algorithm based on reinforcement learning with virtual reality and immersive interaction of industrial metaverse. An industrial metaverse-based intelligent PID tuning system is proposed by combining the above method with the end_edge_cloud collaboration technology of Industrial Internet. The challenging problem that the actual operating PID control system in complex industrial processes cannot be optimized online is solved. Using the energy-intensive equipment, the fused magnesium furnace, as an industrial object, we conducted comparative simulation experiments between the proposed control method and several advanced control methods, as well as industrial experiments for the proposed intelligent system. Simulation experiments demonstrate the effectiveness of the proposed control method. The industrial experimental results indicate that the performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes can be realized, which achieves excellent control effects.

中文翻译：

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基于工业元宇宙的复杂工业过程智能PID优化整定系统


本文提出了虚拟现实场景中工业过程实际PID控制系统的动态性能监测和参数自适应自整定的方法。该方法将基于系统辨识和自适应深度学习的PID控制过程的数字孪生模型和基于强化学习的PID整定智能算法与工业虚拟现实和沉浸式交互相结合。将上述方法与工业互联网端边云协同技术相结合，提出了一种基于工业元宇宙的智能PID整定系统。解决了复杂工业过程中实际运行的PID控制系统无法在线优化的难题。以高耗能设备电熔镁炉为工业对象，对所提出的控制方法与几种先进控制方法进行了对比仿真实验，并对所提出的智能系统进行了工业实验。仿真实验证明了所提出控制方法的有效性。工业实验结果表明，可以实现虚拟现实场景下工业过程实际PID控制系统的性能监测和参数自适应自整定，取得了良好的控制效果。