Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm
Source abstract
Abstract
It is necessary to upgrade and transform the sorting equipment in the industrial production line. In order to improve production efficiency and reduce labor intensity, a high-speed lightweight parallel mechanism control system for the high-speed sorting and packaging of light items was studied. A fuzzy PID controller based on genetic algorithm (GA) optimization is proposed according to the nonlinear and strong coupling characteristics of the parallel mechanism (PM) control system. The inverse kinematic analysis was conducted to map the workspace trajectory tracking problem to the joint space. It was transformed into the trajectory planning and solving problems in the joint space. The motion trajectory was obtained utilizing quintic polynomial interpolation. Finally, the servo control system model was established, and the PID control parameters were optimized and self-tuned by the GA. They were applied to the fuzzy PID controller for simulation experiments. The simulation results showed that the GA-optimized fuzzy PID control system compared with the fuzzy PID control system had a 23.39% shorter rise time, 22.32% less regulation time, and 7.18% less steady-state error. The control system had a good dynamic and steady-state performance.
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Research context
OpenAlex topics: Iterative Learning Control Systems · Advanced Control Systems Design · Industrial Automation and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
Jiangxi Province Key Laboratory of Precision Drive and Control, Nanchang Institute of Technology, Nanchang 330099, China
Jiangxi Institute of Mechanical Science, Nanchang 330002, China
College of Intelligent Manufacturing, Jiangxi Technical College of Manufacturing, Nanchang 330095, China
Funding
Education Department of Jiangxi Province · Key Scientific and Technological Support Topics in Jiangxi Province
References (30)
Show source-provided references
1. Kong, X., Gosselin, C.M., and Richard, P.-L. (2006, January 10–13). Type synthesis of parallel mechanisms with multiple operation modes. Proceedings of the International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Philadelphia, PA, USA.10.1115/DETC2006-99628 ↗
2. Kinematics Analysis and Simulation of a Novel 3T Parallel Mechanism
3. Optimum settings for automatic controllers
4. A new methodology for designing a fuzzy logic controller10.1109/21.364863 ↗
5. Fuzzy PID controller: Design, performance evaluation, and stability analysis10.1016/S0020-0255(99)00127-9 ↗
6. Adaptive fuzzy PID control strategy for spacecraft attitude control10.1007/s40815-018-0576-2 ↗
7. Zhou, H., Chen, R., Zhou, S., and Liu, Z. (2019). Design and analysis of a drive system for a series manipulator based on orthogonal-fuzzy PID control. Electronics, 8.10.3390/electronics8091051 ↗
8. A new fuzzy PID control system based on fuzzy PID controller and fuzzy control process10.1007/s40815-020-00904-y ↗
9. Some solving methods for a fuzzy multi-point boundary value problem10.1007/s00500-019-03926-3 ↗
10. Intelligent traction control method based on model predictive fuzzy PID control and online optimization for permanent magnetic maglev trains10.1109/ACCESS.2021.3059443 ↗
11. Adaptive ORFWNN-based predictive PID control10.1007/s40815-019-00650-w ↗
12. Pelusi, D. (2011, January 26–27). Optimization of a fuzzy logic controller using genetic algorithms. Proceedings of the 2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.10.1109/IHMSC.2011.105 ↗
13. Designing neural networks to improve timing performances of intelligent controllers10.1080/09720529.2013.821333 ↗
14. Optimal control Algorithms for second order Systems10.3844/jcssp.2013.183.197 ↗
15. Purnama, H.S., Sutikno, T., Alavandar, S., and Subrata, A.C. (2019, January 16–17). Intelligent control strategies for tuning PID of speed control of DC motor: A review. Proceedings of the 2019 IEEE Conference on Energy Conversion (CENCON), Yogyakarta, Indonesia.10.1109/CENCON47160.2019.8974782 ↗
16. Design and analysis of DC motor speed control by GA based tuning of fuzzy logic controller
17. Chao, C.-T., Sutarna, N., Chiou, J.-S., and Wang, C.-J. (2019). An optimal fuzzy PID controller design based on conventional PID control and nonlinear factors. Appl. Sci., 9.10.3390/app9061224 ↗
18. Chao, C.-T., Sutarna, N., Chiou, J.-S., and Wang, C.-J. (2017). Equivalence between fuzzy PID controllers and conventional PID controllers. Appl. Sci., 7.10.3390/app7060513 ↗
19. Flores-Morán, E., Yánez-Pazmiño, W., and Barzola-Monteses, J. (2018, January 28–31). Genetic algorithm and fuzzy self-tuning PID for DC motor position controllers. Proceedings of the 2018 19th International Carpathian Control Conference (ICCC), Szilvasvarad, Hungary.10.1109/CarpathianCC.2018.8399621 ↗
20. Morán, M.E.F., and Viera, N.A.P. (2017, January 16–20). Comparative study for DC motor position controllers. Proceedings of the 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), Salinas, Ecuador.
21. Vijaya, S., Bharathiraja, P., and Nithyanandam, J. (2018, January 28–29). A Performance Comparison of Conventional and New Seven Level Inverter Topology Fed PM Synchronous Motor Using GA Based Fuzzy PID Speed Controller. Proceedings of the 2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), Chennai, India.10.1109/ICCPEIC.2018.8525188 ↗
22. Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm10.1177/01423312211066758 ↗
23. Alouache, A., and Wu, Q. (2018, January 6–8). Genetic algorithms for trajectory tracking of mobile robot based on PID controller. Proceedings of the 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania.10.1109/ICCP.2018.8516587 ↗
24. PID control system analysis, design, and technology10.1109/TCST.2005.847331 ↗
25. An optimal fuzzy PID controller10.1109/41.937407 ↗
26. Genetic algorithms10.1145/234313.234350 ↗
27. Genetic algorithms10.1038/scientificamerican0792-66 ↗
28. Sivanandam, S., and Deepa, S. (2008). Genetic algorithms. Introduction to Genetic Algorithms, Springer.10.1007/978-3-540-73190-0_2 ↗
29. Mahfud, S., Derouich, A., El Ouanjli, N., Mossa, M.A., Motahhir, S., El Mahfoud, M., and Al-Sumaiti, A.S. (2022). Comparative Study between Cost Functions of Genetic Algorithm Used in Direct Torque Control of a Doubly Fed Induction Motor. Appl. Sci., 12.10.3390/app12178717 ↗
30. GA-based multiobjective PID control for a linear brushless DC motor10.1109/TMECH.2003.809136 ↗