The Control System of High-Speed Sweeper Swing Arm Based on BP Neural Network PID

This study addresses the limitations of the traditional open-loop control system employed in sweeper swing arms, which exhibit insufficient dynamic response, weak immunity to interference, and inadequate performance under high-speed sweeping conditions. To overcome these challenges, it proposes the closed-loop control system for high-speed sweepers. Moreover, it introduce a PID algorithm based on the improved BP neural network to address issues associated with local extrema and slow convergence inherent in conventional BP neural networks. Through investigation, it assesses the step response, anti-interference capability, and position-tracking proficiency of the designed closed-loop control system for the high-speed sweeper's swing arm. The simulation of the improved BP-PID controller demonstrates its ability to dynamically adjust PID parameters, leading to enhanced system adaptability, accuracy, and stability.