Title: Synchronous speed control for industrial production line based on BP neural network
Authors: Tianci Pan; Changhong Zhu
Addresses: Department of Information Engineering, Bowen College of Management Guilin University of Technology, No. 317, Yanshan Street, Guilin, Guangxi, 541006, China ' School of Computer Science and Engineering, Guilin University of Aerospace Technology, No. 2, Jinji Road, Guilin, Guangxi, 541004, China
Abstract: In order to overcome the problems of large speed control error and poor anti-interference effect existing in the traditional speed control methods, the paper proposes a speed synchronisation control method of industrial production line based on BP neural network. Firstly, the state of production equipment is adjusted through PLC operation instruction, and amplifier circuit is designed to reduce the influence of signal interference. Then the speed control parameters of the production line are adjusted by adaptive control method, and the parameters are fused by fuzzy control theory. Finally, the speed of the industrial production line is synchronously controlled by BP neural network. The experimental results show that the control error coefficient of this method is always lower than 9%, and the influence of step disturbance signal is low, indicating that this method has good application performance.
Keywords: BP neural network; industrial production lines; speed control; fuzzy control theory; PLC instructions; adaptive control.
International Journal of Manufacturing Technology and Management, 2022 Vol.36 No.2/3/4, pp.127 - 140
Received: 30 Nov 2020
Accepted: 03 Jun 2021
Published online: 30 Jun 2022 *