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Reseach Article

Fuzzy Fine tuning of an Optimized PID Control Scheme for Mobile Robot Trajectory Tracking

by Turki Y. Abdalla
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 19
Year of Publication: 2018
Authors: Turki Y. Abdalla
10.5120/ijca2018917875

Turki Y. Abdalla . Fuzzy Fine tuning of an Optimized PID Control Scheme for Mobile Robot Trajectory Tracking. International Journal of Computer Applications. 181, 19 ( Sep 2018), 15-19. DOI=10.5120/ijca2018917875

@article{ 10.5120/ijca2018917875,
author = { Turki Y. Abdalla },
title = { Fuzzy Fine tuning of an Optimized PID Control Scheme for Mobile Robot Trajectory Tracking },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 181 },
number = { 19 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number19/29971-2018917875/ },
doi = { 10.5120/ijca2018917875 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:06:24.451785+05:30
%A Turki Y. Abdalla
%T Fuzzy Fine tuning of an Optimized PID Control Scheme for Mobile Robot Trajectory Tracking
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 19
%P 15-19
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper present an efficient robust design method of PID control scheme based on using fuzzy logic and particles swarm optimization (PSO) method for trajectory tracking of mobile robot. Two PID controllers are used. Parameters of PID controllers are optimized offline using PSO and fuzzy controller is used for tuning the parameters online . The two optimized PID controllers are used for speed control and azimuth control. The online fuzzy tuning in the designed control scheme work well when there are variations in the plant parameters and changes in operating conditions.

References
  1. G. Mester, "Intelligent Mobile Robot Motion Control in Unstructured Environments," Acta Polytechnica Hungarica ,Vol.07, No.04, 2010.
  2. Y. Wang, S. Wang, R. Tan and Y. Jiang,"Motion control of a wheeled mobile robot using digital acceleration control method", International Journal of Innovative Computing, Information and Control, Vol.09, No.01, pp.387-396, 2013.
  3. H. Ouarda, "Novel Mobile Robot Path planning Algorithm", International Journal of system applications, engineering and development, Vol.04, No.04, 2010.
  4. T. Lee, K. Song ,C. Lee and C. Teng, “Tracking Control of Mobile Robots Using Saturation Feedback Controller”, IEEE Transactions on control system technology , Vol.09, No.2, Taiwan, March 2001.
  5. P. Lahoty and G. Parmar, "A Comparative Study of Tuning of PID Controller using Evolutionary Algorithms", International Journal of Emerging Technology and Advanced Engineering, Vol.03, No.01, 2013.
  6. M. I. Hamzah and Turki Y Abdalla, “ Mobile Robot Navigation using Fuzzy Logic and Wavelet Network” , International Journal of Robotics and Automation, Vol. 3, Nol. 3, 2014.
  7. M. A. Abido, “Optimal design of power-system stabilizers using particleswarm optimization,” IEEE Trans. Energy Conversion, vol. 17, pp.406-413, Sep. 2002.
  8. D. R. Shircliff, "Build A Remote-Controlled Robot", eBook, Copyright © by The McGraw-Hill Companies, 2002.
  9. G. Mester, "Obstacle Avoidance of Mobile Robots in Unknown Environments", SISY, International Symposium on Intelligent Systems and Informatics 24-25 Subotica, Serbia, August, 2007.
  10. A. Albagul and Wahyudi, "Dynamic Modelling and Adaptive Traction Control for Mobile Robots", 30th Annual Conference of the IEEE Industrial Electronics Society, November 2 - 6, Susan, Korea 2004.
  11. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proc.IEEE Int. Conf. Neural Networks , vol. IV, Perth, Australia, 1995,pp.1942–1948.
  12. Z.-L. Gaing, “A particle swarm optimization approach for optimum design of PID controller in AVR system,” IEEE Trans. EnergyConversion, vol. 19, pp. 384-391, June 2004.
  13. H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama, and Y. Nakanishi, “A particle swarm optimization for reactive power and voltage control considering voltage security assessment,” IEEE Trans. on Power Systems, Vol. 15, No. 4, Nov.
  14. AA Ahmed, TY Abdalla, AA Abed, “Path planning of mobile robot by using modified optimized potential field method” Iinternational journal of computer applications,vol. 113, No.4, 2015.
  15. TY Abdalla, AA Abed, AA Ahmed, “Mobile robot navigation using PSO-optimized fuzzy artificial potential field with fuzzy control” Journal of Intelligent & Fuzzy Systems,vol. 32, No.6,. 2017.
  16. TY Abdalla, HA Hairik, AM Dakhil , “Minimization of torque ripple in DTC of induction motor using fuzzy mode duty cycle controller ”, Energy, Power and Control (EPC-IQ), 1st International Conference on, IEEE 2010.
  17. Z.T. Allawi and Turki Y. Abdalla "An Optimal Defuzzification Method for Interval Type-2 Fuzzy Logic Control Scheme" IEEE science and information conference , 2015, London
  18. A. Kaur, A. Kaur, "Comparison of Mamdani-Type and Sugeno-Type Fuzzy Inference Systems for Air Conditioning System", International Journal of Soft Computing and Engineering (IJSCE), Vol.02, 2012
  19. Y. Yang, W. Wang, D. J. Yu and G. Ding, "A fuzzy parameters adaptive PID controller design of digital positional servo system", IEEE Proceeding of the First International Conference on Machine Learning and Cybernetics., pp. 310–314, 2002 ,china.
  20. W H Almutar, " Fuzzy Control Schemes for Active Suspension System" M Sc. thesis, university of Basrah, 2015.
  21. K. Watanabe, J. Tang, M. Nakamura, S. Koga and T. Fukuda, “Mobile Robot Control Using Fuzzy-Gaussian Neural Networks", IEEE/RSJ International Conf. on Robots and system, pp.919-925, Japan, 1993.
Index Terms

Computer Science
Information Sciences

Keywords

Mobile Robot Particles Swarm Optimization fuzzy control PID Controller Trajectory tracking.