Neural Processing Letters

, Volume 38, Issue 2, pp 261–279

Gait Pattern Based on CMAC Neural Network for Robotic Applications

Article

Abstract

The main goal of this paper is to provide a general methodology and a practical approach for the design of gait pattern for biped robotic applications directly usable by researchers and engineers. This approach, which is based on CMAC neural network, is an alternative way in comparison to the traditional Central Pattern Generator. In the proposed method, the CMAC neural networks are used to learn basic motions (e.g. reference gait) and a Fuzzy Inference System allows to merge these reference motions in order to built more complex gaits. The results of our biped robotic applications show how to design a self-adaptive gait pattern according to average velocity and external perturbations.

Keywords

CMAC neural network Fuzzy CMAC Gait pattern 

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References

  1. 1.
    Albus JS (1975) Data storage in the Cerebellar Model Articulation Controller (CMAC). J Dyn Syst Meas Control 97: 228–233CrossRefMATHGoogle Scholar
  2. 2.
    Albus JS (1975) A new approach to manipulator control: the Cerebellar Model Articulation Controller (CMAC). J Dyn Syst Meas Control 97: 220–227CrossRefMATHGoogle Scholar
  3. 3.
    Glanz FH, Miller WT, Kraft LG (1991) An overview of the CMAC neural network. In: IEEE conference on neural networks for ocean engineering, 15–17 August 1991, pp 301–308Google Scholar
  4. 4.
    Yao S, Zhang B (1994) The learning convergence of CMAC in cyclic learning. J Comput Sci Technol 9: 320–328CrossRefGoogle Scholar
  5. 5.
    He C, Xu L, Zhang Y (2001) Learning convergence of CMAC algorithm. Neural Process Lett 14: 61–74CrossRefMATHGoogle Scholar
  6. 6.
    Su S-F, Tao T, Hung T-H (2003) Credit assigned CMAC and its application to online learning robust controllers. IEEE Trans Syst Man Cybern B 33: 202–213CrossRefGoogle Scholar
  7. 7.
    Zhang L, Cao Q, Lee J, Zhao Y (2004) A modified CMAC algorithm based on credit assignment. Neural Process Lett 20: 1–10CrossRefMATHGoogle Scholar
  8. 8.
    Brown M, Harris CJ, Parks PC (1993) The interpolation capabilities of the binary CMAC.. 6: 429–440Google Scholar
  9. 9.
    Hung-Ching L, Ming-Feng Y, Jui-Chi C (2006) CMAC study with adaptive quantization. In: IEEE International conference on systems, man and cybernetics, SMC ’06, 8–11 October, pp 2596–2601Google Scholar
  10. 10.
    Hahn-Ming L, Chih-Ming C, Yung-Feng L (2003) A self-organizing HCMAC neural-network classifier. IEEE Trans Neural Netw 14: 15–27CrossRefGoogle Scholar
  11. 11.
    Minh Nhut N, Daming S, Quek C (2006) FCMAC-BYY: Fuzzy CMAC using Bayesian Ying-Yang learning. IEEE Trans Systems Man Cybern 36: 1180–1190CrossRefGoogle Scholar
  12. 12.
    Menozzi A, Chow MY (1997) On the training of a multi-resolution CMAC neural network. In: Proceedings of the IEEE international symposium on industrial electronics, ISIE ’97, vol 3, 7–11 July, pp 1201–1205Google Scholar
  13. 13.
    Chih-Min L, Te-Yu C (2009) Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems. IEEE Trans Neural Netw 20: 1377–1384CrossRefGoogle Scholar
  14. 14.
    Teddy SD, Lai EM, Quek C (2007) Hierarchically clustered adaptive quantization CMAC and its learning convergence. IEEE Trans Neural Netw 18(6): 1658–1682CrossRefGoogle Scholar
  15. 15.
    Ozawa J, Hayashi I, Wakami N (1992) Formulation of CMAC-fuzzy system. In: IEEE international conference on fuzzy systems, 8–12 March 1992, pp 1179–1186Google Scholar
  16. 16.
    Nguyen MN, Shi D, Quek C (2005) Self-organizing Gaussian Fuzzy CMAC with truth value restriction. In: Third international conference on information technology and applications, ICITA 2005, 4–7 July 2005, pp 185–190Google Scholar
  17. 17.
    Daming S, Nguyen MN, Suiping Z, Guisheng Y (2010) Fuzzy CMAC with incremental Bayesian Ying–Yang learning and dynamic rule construction. IEEE Trans Syst Man Cybern 40: 548–552CrossRefGoogle Scholar
  18. 18.
    Chia-Feng J, Chin-Teng L (1998) An online self-constructing neural fuzzy inference network and its applications. IEEE Trans Fuzzy Syst 6: 12–32CrossRefGoogle Scholar
  19. 19.
    Yeh M-F (2007) Single-input CMAC control system. Neurocomputing 70: 2638–2644CrossRefGoogle Scholar
  20. 20.
    Gáti K, Horváth G, Dobnikar A, Lotric U, Šter B (2011) Using CMAC for mobile robot motion control. Springer, New YorkGoogle Scholar
  21. 21.
    Hsu C-F (2009) Design of intelligent power controller for DC–DC converters using CMAC neural network. Neural Comput Appl 18: 93–103CrossRefGoogle Scholar
  22. 22.
    Lua H-C, Changa J-C, Yehb M-F (2007) Design and analysis of direct-action CMAC PID controller. Neurocomputing 70: 2615–2625CrossRefGoogle Scholar
  23. 23.
    Rudenko O, Bessonov A (2005) CMAC neural network and its use in problems of identification and control of nonlinear dynamic objects. Cybern Syst Anal 41: 647–658MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Rodriguez FO, Yu W, Moreno-Armendariz MA (2008) Nonlinear systems identification via two types of recurrent Fuzzy CMAC. Neural Process Lett 28: 49–62CrossRefGoogle Scholar
  25. 25.
    Xu W, Xia S, Xie H (2004) Application of CMAC-based networks on medical image classification. Adv Neural Netw ISNN 3173/2004: 953–958Google Scholar
  26. 26.
    Jar-Shone Ker, Yau-Hwang Kuo, Rong-Chang Wen, Bin-Da Liu (1997) Hardware implementation of CMAC neural network with reduced storage requirement. IEEE Trans Neural Netw 8(6): 1545–1556CrossRefGoogle Scholar
  27. 27.
    Kun A, Miller WT III (1996) Adaptive dynamic balance of a biped robot using neural networks. In: Proceedings of IEEE international conference on robotics and automation, vol 1, 22–28 April, pp 240–245Google Scholar
  28. 28.
    Benbrahim H, Franklin JA (1997) Biped dynamic walking using reinforcement learning. Robot Auton Syst 22: 283–302CrossRefGoogle Scholar
  29. 29.
    Sabourin C, Bruneau O (2005) Robustness of the dynamic walk of a biped robot subjected to disturbing external forces by using CMAC neural networks. Robot Auton Syst 51: 81–99CrossRefGoogle Scholar
  30. 30.
    Jianjuen H, Pratt J, Pratt G (1999) Stable adaptive control of a bipedal walking; robot with CMAC neural networks. In: Proceedings of IEEE international conference on robotics and automation, vol 2, pp 1050–1056Google Scholar
  31. 31.
    Cembrano G, Wells G, Sardá J, Ruggeri A (1997) Dynamic control of a robot arm using CMAC neural networks. Control Eng Pract 5: 485–492CrossRefGoogle Scholar
  32. 32.
    Zhao H, Sugisaka M (2008) Simulation study of CMAC control for the robot joint actuated by McKibben muscles. Appl Math Comput 203: 457–462MathSciNetCrossRefMATHGoogle Scholar
  33. 33.
    Kara R, Wira P, Kihl H (2000) Robot vision tracking with a hierarchical CMAC controller. In: Proceedings of fourth international conference on Knowledge-based intelligent engineering systems and allied technologies, vol 1, pp 271–274Google Scholar
  34. 34.
    Kim YH, Lewis FL (2000) Optimal design of CMAC neural-network controller for robot manipulators. IEEE Trans Syst Man Cybern C 30: 22–31CrossRefGoogle Scholar
  35. 35.
    Santos CP, Matos V (2011) Gait transition and modulation in a quadruped robot: a brainstem-like modulation approach. Robot Auton Syst 59: 620–634CrossRefGoogle Scholar
  36. 36.
    Sabourin C, Madani K, Bruneau O (2007) Autonomous biped gait pattern based on Fuzzy-CMAC neural networks. Integr Computer-Aided Eng 14: 173–186Google Scholar
  37. 37.
    Yu W, Sabourin C, Madani K, Yan J. Design of footstep planning controller for humanoid robot in dynamic environment. In: IEEE international symposium on knowledge acquisition and modeling, China, Wuhan, December 2008Google Scholar
  38. 38.
    Sabourin C, Madani K, Yu W, Yan J (2008) Obstacle avoidance strategy for biped robot based on fuzzy Q-learning. In: Proceedings of international conference on climbing and walking robots and the support technologies for mobile machines, pp 695–702Google Scholar
  39. 39.
    Yu W, Madani K, Sabourin C (2010) Self-optimizing for the Structure of CMAC neural network. In: IEEE 3rd international symposium on knowledge acquisition and modeling (KAM), pp 432–436Google Scholar
  40. 40.
    Watkins C, Dayan P (1992) Q-learning. Mach Learn 8: 279–292MATHGoogle Scholar
  41. 41.
    Glorennec PY (2000) Reinforcement learning: an overview. In: European symposium on intelligent techniques, Aachen, Germany, pp 17–35Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Images, Signals and Intelligence Systems Laboratory (LISSI/EA 3956), Senart-Fontainebleau Institute of TechnologyUPEC UniversityLieusaintFrance
  2. 2.School of Mechatronic EngineeringNorthwestern Polytechnical UniversityXi’anChina

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