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Gain tuning of position domain PID control using particle swarm optimization

Published online by Cambridge University Press:  10 September 2014

V. Pano  and
P. R. Ouyang
V. Pano
Affiliation:
Department of Aerospace Engineering, Ryerson University, Toronto, Canada
P. R. Ouyang*
Affiliation:
Department of Aerospace Engineering, Ryerson University, Toronto, Canada
*
*Corresponding author. E-mail: pouyang@ryerson.ca
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Summary

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Particle swarm optimization (PSO) is a heuristic optimization algorithm and is commonly used for the tuning of PD/PID-type controllers. In this paper, PSO is applied for control gain tuning of a position domain PID controller in order to improve contour tracking performances of linear and nonlinear contours for a serial multi-DOF robotic manipulator. A new fitness function is proposed for gain tuning based on the statistics of the contour error, and pre-existed fitness functions are also applied for the optimization process, followed by some comparison studies. The PSO tuning technique demonstrated the same effectiveness in position domain controllers as in time domain controllers with the results being quite satisfying with low contour errors for both linear and nonlinear contours, and the proposed fitness function is proved to be on par with the pre-existed fitness functions.

Keywords

Position domain controlPIDParticle swarm optimizationControl gainContour trackingContour error

Information

Type
Articles
Information
Robotica , Volume 34 , Issue 6 , June 2016 , pp. 1351 - 1366
Copyright
Copyright © Cambridge University Press 2014 

References

1.Astrom, K. J. and Hagglund, T., “The future of PID control,” Control Eng. Pract. 9 (11), 1163–1175 (2001).Google Scholar
2.Cominos, P. and Munro, N., “PID controllers: Recent tuning methods and design to specification,” IEEE Proc., Control Theory Appl. 149 (1), 46–53 (2002).Google Scholar
3.Nagaraj, B. and Vijayakumar, P., “A comparative study of PID controller tuning using GA, EP, PSO and ACO,” J. Autom. Mobile Robot. Intell. Syst. 5 (2), 42–48 (2011).Google Scholar
4.Kennedy, J. and Eberhart, R., “Particle Swarm Optimization,” Procceedings of IEEE International Conference on Neural Networks, vol. 4, Perth, Australia (Nov. 27–Dec. 1, 1995) pp. 1942–1948.Google Scholar
5.Airashidi, M. R. and El-Hawary, M. E., “A survey of particle swarm optimization applications in electric power systems,” IEEE Trans. Evol. Comput. 13 (4), 913–918 (2009).Google Scholar
6.Nasri, M., Nezamabadi-pour, H. and Maghfoori, M., “A PSO-based optimum design of PID controller for a linear brushless DC motor,” Word Acad. Sci. Eng. Technol. 26 (40), 211–215 (2007).Google Scholar
7.Giriraj Kumar, S. M., Jayaraj, D. and Kishan, A. R., “PSO-based tuning of a PID controller for a high performance drilling machine,” Int. J. Comput. Appl. 1 (19), 12–18 (2010).Google Scholar
8.Solihin, M., Kamal, M. and Legowo, A., “Optimal PID Controller Tuning of Automatic Gantry Crane Using PSO Algorithm,” Procceeding of the 15th International Symposium on Mechatronics and its Applications, Amman, Jordan (May 27–29, 2008) pp. 1–5.Google Scholar
9.Kumar, C. A., “Multi-objective PID controller based on adaptive weighted PSO with applications to steam temperature control in boilers,” Int. J. Eng. Sci. Technol. 2 (7), 3179–3184 (2010).Google Scholar
10.Lv, C. and Pang, Y., “Improved PD Controller for AUV Based on MPSO,” Proceedings of the IEEE International Asia Conference on Informatics in Control, Automation and Robotics, Bangkok, Thailand (Feb. 1–2, 2009) pp. 24–28.Google Scholar
11.Dorrah, H. T., El-Garhy, A. M. and El-Shimy, M. E., “Design of PSO-Based Optimial Fuzzy PID Controllers for the Two-Coupled Distillation Column Process,” Proceedings of the 14th International Middle East Power Systems Conference, Cairo, Egypt (Dec. 19–21, 2010) pp. 181–188.Google Scholar
12.Zamani, M., Sadati, N. and Ghartemani, M. K., “Design of an H PID controller using particle swarm optimization,” Int. J. Control Autom. Syst. 7 (2), 273–280 (2009).Google Scholar
13.Lin, L. and Wang, F., “Robust PID Controller Design Using Particle Swarm Optimization,” Proceedings of the 7nth Asian Control Conference, IEEE, Hongkong (Aug. 27–29, 2009) pp. 1673–1678.Google Scholar
14.Ouyang, P. R., Huang, J., Zhang, W. J. and Dam, T., “Contour tracking control in position domain,” Mechatronics 22 (7), 934–944 (2012).Google Scholar
15.Ouyang, P. R., Pano, V. and Dam, T., “PID position domain control for contour tracking,” Int. J. Syst. Sci. DOI: http://dx.doi.org/10.1080/00207721.2013.775385 (2013).Google Scholar
16.Ouyang, P. R., Pano, V. and Acob, J., “Position domain contour control for multi-DOF robotic system,” Mechatronics 23 (18), 1061–1071 (2013).Google Scholar
17.Ouyang, P. R., Dam, T. and Pano, V., “Cross-coupled PID control in position domain for contour tracking,” Roboticia DOI: http://dx.doi.org/10.1017/S0263574714000769 (2014).Google Scholar
18.Craig, J., Introduction to Robotis: Mechanics and Control (Peason Prentice Hall, Upper Saddle River, New Jersey, 2005).Google Scholar
19.Perez, R. E. and Behdinan, K., “Particle swarm approach for structural design optimization,” Comput. Struct. 85 (19–20), 1579–1588 (2007).Google Scholar
20.Ebbesen, S., Kiwitz, P. and Guzzella, L., “A Generic Particle Swarm Optimization Matlab Function,” Proceedings of the IEEE American Control Conference, Montreal, Canada (Jun. 27–29, 2012) pp. 1519–1524.Google Scholar
21.Zhang, Y., Qiao, F., Lu, J., Wang, L. and Wu, Q., “Performance Criteria Research on PSO-PID Control Systems,” International Conference on Intelligent Computing and Cognitive Informatics, Kuala Lampur, Malaysia (Jun. 22–23, 2010) pp. 316–320.Google Scholar
22.Lynch, K. M., Shiroma, N. and Tanie, K., “Colllision-free trajectory planning for a 3-DOF robot with a passive joint,” Int. J. Robot. Res. 19 (12), 1171–1184 (2000).Google Scholar
23.Armstrong-Helouvry, B., Dupont, P. and Canudas De Wit, C., “A survey of models, analysis tools and compensation methods for the control of machines with friction,” Automatica 30 (7), 1083–1138 (1994).Google Scholar
24.Yeh, S. S. and Hsu, P. L., “A New Approach to Biaxial Cross-Coupled Control,” Proceedings of the 2000 IEEE International Conference on Control Applications, Anchorage, USA (Sep. 25–27, 2000) pp. 168–173.Google Scholar
25.Cheng, M. Y. and Lee, C.C., “Motion controller design for contour-following tasks based on real-time contour error estimation,” IEEE Trans. Ind. Electron. 54 (3), 1686–1695 (2007).Google Scholar
26.Storn, R. and Price, K., “Differential evolution –- a simple and efficient heuristic for global optimization over continuous spaces,” J. Glob. Optim. 11 (4), 341–359 (1997).Google Scholar
27.Panda, S., “Robust coordinated design of multiple and multi-type damping controller using differential evolution algorithm,” Int. J. Electr. Power Energy Syst. 33 (4), 1018–1030 (2011).Google Scholar