Collaborative Optimization Design of Automated Production Line Motion Control System for Intelligent Manufacturing

With the proposal of “Industry 4.0”, the automation of intelligent manufacturing production lines and their control systems have become particularly important. In response to the problems of large trajectory tracking errors and weak control performance in traditional automated production lines and their control systems, research proposes using iterative learning control for production line automation optimization. By analyzing the structural composition of the automated production line, a motion control system is established to optimize the iterative learning control structure. This method utilizes iterative learning control algorithms to track trajectories and sets parameters for the weight matrix of the control system. It then combines multi-objective optimization to control multiple objectives of the production line and coordinates the control system. The results indicate that the proposed iterative learning control has lower contour error compared to PID control and fuzzy control. The maximum contour error of the iterative learning control is 0.0248, and the maximum root mean square error is 0.0756. The use of multi-objective optimization for a collaborative control system can effectively improve the stability of the control system. Overall, the method proposed by the research can effectively control the motion of automated production lines, achieve good control performance, and effectively improve the collaborative control of automated production line systems.