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Optimal Fuzzy Controller Design Using an Evolutionary Strategy-Based Particle Swarm Optimization for Redundant Wheeled Robots

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An Erratum to this article was published on 14 May 2016

Abstract

This paper presents an optimal fuzzy controller design method using an evolutionary strategy-based particle swarm optimization (ESPSO) to four-wheeled redundant mobile robots, called FC-ESPSO. In comparison with conventional fuzzy controllers, this approach takes the advantages of evolutionary strategy, fuzzy control, and PSO, thereby obtaining better population diversity, avoiding premature convergence, and achieving self-adaptive redundant control. This hybrid computing is then applied to develop an optimal fuzzy controller of redundant wheeled robots. The optimal parameters of the dynamic controller are self-adaptive via the proposed FC-ESPSO to resolve trajectory-tracking problem. Simulation results are given to exhibit the merits of the proposed FC-ESPSO than other related control methods for redundant wheeled robots.

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References

  1. Juang, C.F., Chang, Y.C.: Evolutionary group-based particle swarm-optimized fuzzy controller with application to mobile robot navigation in unknown environments. IEEE Trans. Fuzzy Syst. 19(2), 379–392 (2011)

    Article  Google Scholar 

  2. Wang, Z., Zhang, Z., Mao, J.: Adaptive tracking control based on online LS-SVM identifier. Int. J. Fuzzy Syst. 14(2), 330–336 (2012)

    MathSciNet  Google Scholar 

  3. Yu, J.J., Zhang, K.J., Fei, S.M.: Adaptive fuzzy tracking control of a class of stochastic nonlinear systems with unknown dead-zone input. Int. J. Fuzzy Syst. 10(1), 18–23 (2008)

    MathSciNet  Google Scholar 

  4. Zhang, L., Tong, S., Li, Y.: Adaptive fuzzy output-feedback control with prescribed performance for uncertain nonlinear systems. Int. J. Fuzzy Syst. 16(2), 212–221 (2014)

    Article  MathSciNet  Google Scholar 

  5. Weitian, C., Saif, M.: A novel fuzzy system with dynamic rule base. IEEE Trans. Fuzzy Syst. 13(5), 569–582 (2005)

    Article  Google Scholar 

  6. Celikyilmaz, A., Turksen, I.B.: Enhanced fuzzy system models with improved fuzzy clustering algorithm. IEEE Trans. Fuzzy Syst. 16(3), 779–794 (2008)

    Article  Google Scholar 

  7. Goharimanesh, M., Lashkaripour, A., Shariatnia, S., Akbari, A.: Diabetic control using genetic fuzzy-PI controller. Int. J. Fuzzy Syst. 16(2), 133–138 (2014)

    MathSciNet  Google Scholar 

  8. Sharma, D., Chatterjee, A., Rakshit, A.: A hybrid approach for design of stable adaptive fuzzy controllers employing Lyapunov theory and particle swarm optimization. IEEE Trans. Fuzzy Syst. 17(2), 329–342 (2009)

    Article  Google Scholar 

  9. Kennedy, J. Eberhart, R.C.: Particle swarm optimization. Proceedings of IEEE International conference on neural networks. 1942–1948, Nov (1995)

  10. Juang, C.F.: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans. Syst. Man Cybern. B Cybern. 34(2), 997–1006 (2004)

    Article  Google Scholar 

  11. Wai, R.J., Lee, J.D., Chuang, K.L.: Real-time PID control strategy for maglev transportation system via particle swarm optimization. IEEE Trans. Ind. Electron. 58(2), 629–646 (2011)

    Article  Google Scholar 

  12. Song, Y., Chen, Z., Yuan, Z.: New chaotic PSO-based neural network predictive control for nonlinear process. IEEE Trans. Neural Netw. 18(2), 595–601 (2007)

    Article  Google Scholar 

  13. Dong, W., Zhou, M.: Gaussian classifier-based evolutionary strategy for multimodal optimization. IEEE Trans. Neural Netw. Learn. Syst. 25(6), 1200–1216 (2014)

    Article  Google Scholar 

  14. Yin, J., Wang, Y., Hu, J.: A new dimensionality reduction algorithm for hyperspectral image using evolutionary strategy. IEEE Trans. Ind. Inf. 8(4), 935–943 (2012)

    Article  Google Scholar 

  15. Hay, J., Loo, K.: Evolutionary strategy search algorithm for fast block motion estimation. Electron. Lett. 42(15), 854–856 (2006)

    Article  Google Scholar 

  16. Huang, H.C.: Intelligent motion control for four-wheeled holonomic mobile robots using FPGA-based artificial immune system algorithm. Adv. Mech. Eng. 2013, 1–11 (2013)

    Google Scholar 

  17. Rameesha, W., Munasinghe, R.: Development of a holonomic mobile robot for field applications. IEEE Int. Conf. Ind. Inf. Syst. 499–504 (2009)

  18. Asama, H., Sato, M., Bogoni, L., Kaetsu, H., Matsumoto, A., Endo, I.: Development of an omni-directional mobile robot with 3 DOF decoupling drive mechanism. IEEE Int. Conf. Robot. Autom. 2, 1925–1930 (1995)

    Google Scholar 

  19. Huang, H.C., Huang, C.Y.: Trajectory tracking control of four-wheeled mobile robots using AIS metaheuristic algorithm. International Conference on Fuzzy Theory and Its Applications (iFUZZY 2014). Nov 26–28, Taiwan, (2014)

  20. Hashemi, E., Jadidi, M.G., Jadidi, N.G.: Model-based PI-fuzzy control of four-wheeled omni-directional mobile robots. Robot. Auton. Syst. 59, 930–942 (2011)

    Article  Google Scholar 

  21. Song, J.B., Byun, K.S.: Design and control of a four-wheeled omnidirectional mobile robot with steerable omnidirectional wheels. J. Robot. Syst. 21(4), 193–208 (2004)

    Article  MATH  Google Scholar 

  22. Wada, M., Asada, H.: Design and control of a variable footprint mechanism for holonomic omnidirectional vehicles and its application to wheelchairs. IEEE Trans. Robot. Autom. 15(6), 978–989 (1999)

    Article  Google Scholar 

  23. Tahboub, V, Asada, H.: Dynamic analysis and control of a holonomic vehicle with continuously variable transmission. EEE Int. Conf. Robot. Autom. 2466–2472 (2000)

  24. Indiveri, G.: Swedish wheeled omnidirectional mobile robots: kinematics analysis and control. IEEE Trans. Rob. 25(1), 164–171 (2009)

    Article  Google Scholar 

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Acknowledgments

The authors gratefully acknowledge financial support from the Ministry of Science and Technology, Taiwan, R.O.C., under Grant MOST 103-2221-E-197-028.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Ilan University, Yilan, Taiwan

    Hsu-Chih Huang & Chih-Hao Chiang

  2. Graduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei, Taiwan

    Sendren Sheng-Dong Xu

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  1. Hsu-Chih Huang
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  2. Sendren Sheng-Dong Xu
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  3. Chih-Hao Chiang
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Correspondence to Hsu-Chih Huang.

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Huang, HC., Xu, S.SD. & Chiang, CH. Optimal Fuzzy Controller Design Using an Evolutionary Strategy-Based Particle Swarm Optimization for Redundant Wheeled Robots. Int. J. Fuzzy Syst. 17, 390–398 (2015). https://doi.org/10.1007/s40815-015-0055-y

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