Genetic Algorithm Tuning PID Control of Magnetic Powder Clutch

Abstract:

The linear relationship between transfer torque and exciting current of magnetic powder clutch easily achieves to the electronic control and are widely used on the passenger cars. This article established the drive circuit model based on the experiments of excitation current transfer torque for magnetic powder clutch. Aiming at the nonlinear problems of the drive circuit, the article designed a current controller of magnetic powder clutch, which used method of genetic algorithms tuning PID control parameter. The simulation results showed that the drive control dynamic based on the genetic algorithms tuning PID was better than the traditional PID control, and the system had better adaptability.

• Periodical: Advanced Materials Research (Volumes 712-715)
• Pages: 2216-2222
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.712-715.2216
• Online since: June 2013
• Authors: Xiao Gang Wu, Rui Xue Liu, Nan Nan Ding
• Keywords: Current Control, Genetic Algorithm (GA), Magnetic Powder Clutch, PID Control

References

[1] WEI Yingjun, CHANG Siqin. Research on fuzzy control technology applied to magnetic powder clutch during vehicle starting[J]. China Mechanical Engineering, vol.16, no.11, pp.1029-1033, 2005.

[2] Cheng WANG, Shanzhen XU. Study on performance of vehicle magnetic powder clutch with permanent magnet[C]. Proceedings 4th International Conference on Information and Computing, pp.208-211, Phuket Island, Thailand, 2011.

DOI: 10.1109/icic.2011.140

[3] Shanzhen XU, Cheng WANG. Structure analysis and design of magnetic powder clutch for vehicle based on analysis of electromagnetic[C]. 2012 2nd International Conference on Consumer Electronics, Communications and Networks, pp.2870-2873, Three Gorges, China, 2012.

DOI: 10.1109/cecnet.2012.6201990

[4] Hua LI, Jin YAO and Shijia ZHAO. High-efficiency hydraulic torque-converting transmission system[J]. Journal of Mechanical Engineering, vol.46, no.13, pp.116-121, (2010)

DOI: 10.3901/jme.2010.13.116

[5] Xiaogang WU, Xudong WANG and Tengwei YU. A research on the fuzzy control of magnetic powder clutch based on adaptive weight particle swarm optimization[J]. Automotive Engineering, vol.32, no.6, pp.510-514, (2010)

DOI: 10.1109/ccdc.2008.4597872

[6] Xiaogang WU, Xudong WANG. Fuzzy control research of magnetic powder clutch based on variable learning factor particle swarm optimization[C]. 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, vol.4, pp.117-120, 2010.

DOI: 10.1109/cmce.2010.5610195

[7] Pyung Hun Chang, Je Hyung Jung. A systematic method for gain selection of robust PID control for nonlinear plants of second-order controller canonical form[J]. IEEE Transactions on Control Systems Technology, vol.17, no.2, pp.473-483, 2009.

DOI: 10.1109/tcst.2008.2000989

[8] M. Vagia, A. Tzes. Robust PID control design for an electrostatic micromechanical actuator with structured uncertainty[J]. IET Control Theory Applications, vol.2, no.5, pp.365-373, 2008.

DOI: 10.1049/iet-cta:20070284

[9] Carl-Magnus Fransson, Torsten Wik and Bengt Lennartson, et al. Nonconservative robust control: optimized and constrained sensitivity functions[J]. IEEE Transactions on control systems technology, vol.17, no.2, pp.298-308, 2009.

DOI: 10.1109/tcst.2008.924564

[10] Eranda Harinath, George K. I. Mann. Design and tuning of standard additive model based fuzzy PID controllers for multivariable process systems[J]. IEEE Transactions on systems, Man and Cybernetics- Part B: Cybernetics, vol.38, no.3, pp.667-674, 2008.

DOI: 10.1109/tsmcb.2008.919232

[11] J. L. Meza, V. Santibanez and R. Soto, et al. Fuzzy self-tuning PID semiglobal regulator for robot manipulators[J]. IEEE Transactions on Industrial electronics, vol. 59, no.6, pp.2709-2717, (2012)

DOI: 10.1109/tie.2011.2168789

[12] D. Devaraj, B. Selvabala. Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral-derivative controller in automatic voltage regulator system[J]. IET Generation, Transmission & Distribution, vol.3, no.7, pp.641-649, 2009.

DOI: 10.1049/iet-gtd.2008.0287

[13] M. B. B. Sharifian, A. Mirlo and J. Tavoosi, et al. Self-Adaptive RBF neural network PID controller in linear elevator[C]. 2011 International Conference on Electrical Machines and Systems, pp.1-4, (2011)

DOI: 10.1109/icems.2011.6073387

[14] Wanzhao WANG, Xingtao ZHAO and Yanping SONG. Application of fuzzy-RBF-based PID controller in superheated steam temperature control system[J]. Electric Power Automation Equipment, vol.27, no.11, pp.48-50, 2007.

[15] Jing ZHANG, Xuehong PEI. Self tuning PID controller of RBF based on-line identification neural network[J]. Electric Machines and Control, vol.13, no.S1, pp.157-160, 2009.

[16] Roeva Olympia, Slavov Tsonyo. PID controller tuning based on metaheuristic algorithms for bioprocess control[J]. Biotechnology and Biotechnological Equipment, vol.26, no.5, pp.3267-3277, 2012.

DOI: 10.5504/bbeq.2012.0065

[17] Altinten Ayla, Ketevanlioglu Fazil and Erdogan Sebahat, et al. Self-tuning PID control of jacketed batch polystyrene reactor using genetic algorithm[J]. Chemical Engineering Journal, vol.138, no.1-3, pp.490-497, 2008.

DOI: 10.1016/j.cej.2007.07.029

[18] Wu Chia-Ju, Ko Chia-Nan and Fu Yu-Yi, et al. A genetic-based design of auto-tuning fuzzy PID controllers[J]. International Journal of Fuzzy Systems, vol.11, no.1, pp.49-58, 2009.

[19] Ali Hashim, Ullah Ikram and Irfan M. et al. Genetic algorithm based PID tuning for controlling paraplegic humanoid walking movement[J]. International Journal of Computer Science Issues, vol.9, no.4, pp.275-285, 2012.

[20] LIU Jinkun. The MATLAB Simulation of advanced PID control(3rd edition)[M]. Beijing: Publishing House of Electronics Industry，(2012)