Operation control of visualization VR teaching platform for mechanical manufacturing professional course under iterative learning PID algorithm

To address the issue of operational instability in the VR visualization teaching platform for mechanical manufacturing courses—caused by significant errors in system variables during complex tasks such as virtual environment rendering, interactive operations, and data transmission/processing—an iterative learning PID control algorithm is proposed. This study first examines the operational framework of the platform, followed by a detailed analysis of the key variable factors influencing its control performance. According to the relevant variable factors to determine the platform stable operation control target parameters, set the optimal value of control parameters, joint iterative learning control algorithm and PID algorithm, design iterative learning PID controller, the collected platform operation variable values into the controller, through the controller to make the teaching platform in the daily operation, constantly converge to the optimal control parameters, to prevent the platform operation variable error caused by the platform operation space-time loss of control, to realize the teaching platform operation control, to prevent the platform operation variable error caused by the platform operation space-time loss of control. The controller ensures the teaching platform converges to optimal control parameters during daily operation, preventing instability caused by system variable errors, and realizes the stable operation of the teaching platform. The experimental results show that this method is highly accurate and effective in controlling the operation of the teaching platform.