Improved PID Algorithm-intelligent Detection System of Tightening Machine

Main Article Content

Yun Bai, Tengfei Jing


As the level of automation in manufacturing industry continues to improve, intelligent tightening technology is widely used in many fields. Research on torque, force and angle control technology for tightening machines focuses on designing sensitive detection systems, constructing control models and implementing accurate control of tightening parameters. This article proposes an optimized PID algorithm to improve tightening accuracy and stability. At the same time, an intelligent tightening machine monitoring system will be designed and developed to achieve online monitoring and control, ensuring tightening quality and stability. The system will use the latest technologies, including big data, cloud computing and artificial intelligence, to further optimize the tightening effect. The overshoot of PID control optimized by PSO-GA hybrid algorithm is 8.367% , which is far less than that of PID control alone, it is shown that the PSO-GA hybrid algorithm has higher accuracy and better robustness.

Article Details

Author Biography

Yun Bai, Tengfei Jing

[1],*Yun Bai

2Tengfei Jing


[1] Department of Vehicle Engineering, Chongqing Industry Polytechnic College, Chongqing, Chongqing 401120

2 Department of Vehicle Engineering, Chongqing Industry Polytechnic College, Chongqing, Chongqing 401120

*Corresponding author: Yun Bai

Copyright © JES 2024 on-line :


Li B L, Zhang z c, Chen Rong, research and application of screw tightening technology. Equipment Manufacturing Technology, 2020(10) : 167-169.(in Chinese)

Xu Peng, Li Yang, Gu Huan, etc. . Design and implementation of automatic production line for marine needle body shaft parts. Manufacturing Technology and machine tools, 2017(8) : 124-127.(in Chinese)

Marsching, Fang Xifeng, Li Zhiduo, design of automatic control system of four-blade diaphragm based on PLC. Manufacturing automation, 2021,43(9) : 97-100.(in Chinese)

Chen Hongge. Joint space impedance control based on torque sensor. Mechanical Manufacturing, 2019,57(05) : 30-33.0

HUANG De-yin. Research on Dynamics and Control of Excavator Hydraulic Working System. Fuzhou: School of Mechanical and Electrical Engineering and Automation, Fuzhou University, 2016

Ye H , Yang L , Liu X . Optimizing Weight and Threshold of BP Neural Network Using SFLA: Applications to Nonlinear Function Fitting. 2013:73-81.

JIN Mei, WU Chong-you, HAN Shu-qin. The application and development trend of hydraulic transmission and control technology in agricultural machinery. Machine Tool and Hydraulic, 2017,45 (23): 172-176.

Liu Yimin. Research on PID control method based on improved BP neural network. Chinese Academy of Sciences Research, College of Students (Xi'an Institute of Optics and Precision Machinery-RRB- , 2007.

Zhou Feng. Application of neural network PID control in industrial process control. Hefei University of Technology, 2006.

Anita. Design and simulation of intelligent adaptive PID/PD controller. Harbin Institute of Technology, 2014.

Lau Leng Leng. Research and application of PID parameter tuning technology. Zhengzhou University, 2010.

Yu Ming-li. Study on deterministic performance evaluation of PID controller. Dalian University of Technology, 2015.

Deng Huachang. PID parameter optimization based on hybrid genetic algorithm and its application in level control. Wuhan University of Science and Technology, 2009.

HO chi-keung. Study on PID controller parameter tuning method and its application. Zhejiang University, 2005.

Zhao Qing. Design and implementation of transformer fault diagnosis system based on neural network. Zhengzhou University, 2014.

Lu Qing, Zhu Long Fei, Wang Zi-ping. Research on servo-press controller based on Fractional-order control algorithm. Electromechanical information, 2017(24) : 31-33.

Klein M , Cangelosi A , Wennekers T . What must come down goes up - the effect of noise on weights in spike-timing-dependent plasticity. BMC Neuroence, 2015, 16(Suppl 1):P283.

Dragan Antić, Miroslav Milovanović, Saša Nikolić, et al. Simulation Model of Magnetic Levitation Based on NARX Neural Networks. international journal of intelligent systems & applications, 2013, 5(5):25-32.

Liu Shengqian, Zhang Liping, Sun Xuan, Zhong Zhixian. Modeling and simulation of double closed-loop DC speed governing system based on MATLAB. Journal of Guilin University of Technology Science (No. 2) : 378-382.

Guo Zhijian, Zhang Shourong. Simulation of double closed-loop DC speed governing system based on MATLAB. Industrial Control Computer, 2015(2) : 127-128.