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PSO-based Motion Fuzzy Controller Design for Mobile Robots

PSO-based Motion Fuzzy Controller Design for Mobile Robots. International Journal of Fuzzy Systems, Vol.10, No. 1, March 2008. Master : Juing-Shian Chiou Student : Yu-Chia Hu( 胡育嘉 ) PPT : 100% 製作. Decision and control lab. Outline. Abstract Introduction

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PSO-based Motion Fuzzy Controller Design for Mobile Robots

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  1. PSO-based Motion Fuzzy Controller Design for Mobile Robots International Journal of Fuzzy Systems, Vol.10, No. 1, March 2008 Master : Juing-Shian Chiou Student : Yu-Chia Hu(胡育嘉) PPT : 100%製作 Decision and control lab.

  2. Outline • Abstract • Introduction • Motion Fuzzy Control Structure • PSO-based Fuzzy Controller Design Method • Simulation Results • Conclusions • References

  3. Abstract • A motion control structure with a distance fuzzy controller and an angle fuzzy controller is proposed to determine velocities of the left-wheeled motor and right-wheeled motor of the two-wheeled mobile robot. • A PSO-based method is proposed to automatically determine appropriate membership functions of these two fuzzy systems so that the controlled robot can move to any desired position effectively in a two-dimensional space.

  4. Introduction(1/3) • In this paper, a PSO-based motion fuzzy controller design method is proposed to automatically determine appropriate membership functions of fuzzy systems to control a two-wheeled mobile robot so that it move efficiently in a two-dimensional space.

  5. Introduction(2/3) • In Section 2, a two-wheeled mobile robot is described and a motion fuzzy control structure is proposed to determine velocities of its left-wheeled motor and right-wheeled motor. • In Section 3, a PSO-based fuzzy controller design method with a ratio coefficient coding method and a variable fitness function is proposed to automatically select the input and output membership functions of these two fuzzy systems.

  6. Introduction(3/3) • In Section 4, some results simulated in the 3D robot soccer simulator of FIRA [19] are presented to illustrate the efficiency of the proposed method • Finally, some conclusions are made in Section 5.

  7. Motion Fuzzy Control Structure(1/9)

  8. Motion Fuzzy Control Structure(2/9)

  9. Motion Fuzzy Control Structure(3/9)

  10. Motion Fuzzy Control Structure(4/9)

  11. Motion Fuzzy Control Structure(5/9) : if is AND is ,then is If is and is ,then is : Where and are input variables, Are output variables

  12. Motion Fuzzy Control Structure(6/9)

  13. Motion Fuzzy Control Structure(7/9) Where and are respectively described by

  14. Motion Fuzzy Control Structure(8/9)

  15. Motion Fuzzy Control Structure(9/9)

  16. PSO-based Fuzzy Controller Design Method(1/11) • The PSO algorithm is a computation technique proposed by Kennedy and Eberhart. Its development was based on observations of the social behavior of animals such as bird flocking and fish schooling of the swarm theory.

  17. PSO-based Fuzzy Controller Design Method(2/11) Step 1: Initialize the PSO algorithm by setting , g=1, the maximum number of generation (G), the number of particles (N), and four parameter values of , and .

  18. PSO-based Fuzzy Controller Design Method(3/11) Step 2: Generate the initial position vector and the initial velocity vector of N particles randomly by and

  19. PSO-based Fuzzy Controller Design Method(4/11) Step 3: Calculate the fitness value of each particle in the g-th generation by where is the fitness function.

  20. PSO-based Fuzzy Controller Design Method(5/11) Step 4: Determine and for each particle by

  21. PSO-based Fuzzy Controller Design Method(6/11) Step 5: Find an index q of the particle with the highest fitness by and determineand and by and and where is the position vector of the particle with the global best fitness value from the beginning to the current generation.

  22. PSO-based Fuzzy Controller Design Method(7/11) • Step 6: If g=G, then go to Step 12, Otherwise, go to Step 7. Step 7: Update the velocity vector of each particle by

  23. PSO-based Fuzzy Controller Design Method(8/11) Step 8: Check the velocity constraint by

  24. PSO-based Fuzzy Controller Design Method(9/11) • Step 9: Update the position vector of each particle by Where is the current position vector of the h-th particle in the g-th generation. is the next position vector of the h-th particle in the (g+1)-th generation.

  25. PSO-based Fuzzy Controller Design Method(10/11) Step 10: Bound the updated position vector of each particle in the searching range by

  26. PSO-based Fuzzy Controller Design Method(11/11) • Step 11: Let g=g+1 and go to Step 3. • Step 12: Determine the corresponding fuzzy controller based on the position of the particle with the best fitness value .

  27. Simulation Results(1/3)

  28. Simulation Results(2/3)

  29. Simulation Results(3/3)

  30. Conclusions • A PSO-based motion fuzzy controller design method is proposed to determine velocities of the left-wheeled motor and right-wheeled motor of the two-wheeled mobile robot so that the controlled robot can move to any desired position effectively in a two-dimensional space. • In the practical application, the proposed fuzzy controller design method has been successfully applied to control two-wheeled mobile robots for the actual FIRA robot soccer tournament and it also has a good performance.

  31. References • [1] M. Bowling and M. Veloso, “Motion control in • dynamic multi-robot environments,”IEEE International • Symposium on Computational Intelligence in • Robotics and Automation, pp. 168-173, 1999. • [2] W. R. Hwang and W. E. Thompson, “Design of • intelligent fuzzy logic controllers using genetic algorithms,” • IEEE World Congress on Computational • Intelligence, pp. 2144-1388, 1994. • [3] T. H. Lee, F. H. F. Leung, and P. K. S. Tam, ”Position • control for wheeled mobile robots using a • fuzzy logic controller," IEEE International Conference • on Industrial Electronics Society, pp. • 525-528, 1999. • [4] Y. Lee and S. H. Zak, “Genetic fuzzy tracking controllers • for autonomous ground vehicles,”American • Control Conference, pp. 2144-2149, 2002. • [5] C. Lin, A. B. Rad, W. L. Chan, and K. Y. Cai, “A • robust fuzzy PD controller for automatic steering • control of autonomous vehicles,”IEEE International • Conference on Fuzzy Systems, pp. 549-554, 2003

  32. References • [6] F. Solc and B. Honzik, “Modeling and control of a • soccer robot,”IEEE International Workshop in • Advanced Motion Control, pp. 506-509, 2002. • [7] C. C. Wong, H. Y. Wang, S. A. Li, and C. T. Cheng • “Fuzzy controller designed by GA for two-wheeled • mobile robots,”International Journal of Fuzzy • Systems, vol. 9, no. 1, pp. 22-30, 2007 • [8] W. L. Xu, S. K. Tso, “Real time self reaction of a • mobile robot in unstructured environments using • fuzzy reasoning,”Engineering Applications of Artificial • Intelligence, vol. 9, no. 5, pp. 475-485, • 1996. • [9] C. C. Chen and C. C. Wong, “Self-generating • rule-mapping fuzzy controller design using a genetic • algorithm,”IEE Proceedings: Control Theory • and Applications, vol. 149, pp. 143-148, 2002. • [10] C. C. Wong and S. M. Her, “A self-generating • method for fuzzy system design,”Fuzzy Sets and • Systems, vol.103, no.1, pp.13-25, 1999. • [11] C. C. Wong and C. C. Chen, “A GA-based method • for constructing fuzzy systems directly from numerical • data,”IEEE Transactions on Systems, Man • and Cybernetics-Part B: Cybernetics, vol. 30, no. 6, • pp. 904-911, 2000. • [12] C. C. Wong, B. C. Lin, S. A. Lee, and C. H. Tsai, • “GA-based fuzzy system design in FPGA for an • omni-directional mobile robot,”Journal of Intelligent • & Robotic Systems, vol.44, no.4, pp.327-347, • 2005.

  33. References • [13] J. Kennedy and R. Eberhart, “Particle swarm optimization,” • IEEE International Conference Neural • Network, pp. 1942-1948. 1995 • [14] R. Eberhart and J. Kennedy, “A new optimizer • using particle swarm theory,”Sixth International • Symposium on Micro Machine and Human Science, • Nagoya Japan, pp.39-43,1995. • [15] J. Kennedy, “The particle swarm: Social adaptation • of knowledge,”International Conference on Evolutionary • Computation, pp. 303-308, 1997. • [16] Z. L. Gaing, “A particle swarm optimization approach • for optimum design of PID controller in • AVR system,”IEEE Trans. on Energy Conversion, • vol. 19, no. 2, pp.384-391, 2004. • [17] M. Clerc and J. Kennedy, “The particle • swarm-explosion, stability, and convergence in a • multidimensional complex space,”IEEE Transactions • on Evolutionary Computation, vol. 6, no. 1, • pp.58-73, 2002. • [18] F. Ye, C. Y. Chen, and H. M. Feng, “Automatic • evolutional clustering-based fuzzy modeling system • design,”International Journal of Fuzzy Systems, • vol. 7, no.4, pp. 182-190, 2005. • [19] http://www.fira.net/soccer/simurosot/overview.html

  34. Thank of your attention

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