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Robust Recurrent Neural Network Control of Biped Robot

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Abstract

In this paper, a recurrent neural network (RNN) control scheme is proposed for a biped robot trajectory tracking system. An adaptive online training algorithm is optimized to improve the transient response of the network via so-called conic sector theorem. Furthermore, L 2-stability of weight estimation error of RNN is guaranteed such that the robustness of the controller is ensured in the presence of uncertainties. In consideration of practical applications, the algorithm is developed in the discrete-time domain. Simulations for a seven-link robot model are presented to justify the advantage of the proposed approach. We give comparisons between the standard PD control and the proposed RNN compensation method.

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References

  1. Yildirim, S.: Design of adaptive robot control system using recurrent neural network. J. Intell. Robot. Syst. 44(3), 247–261 (2005)

    Article  Google Scholar 

  2. Loo, C.K., Mandava, R., Rao, M.V.C.: A hybrid intelligent active force controller for articulated robot arms using dynamic structure neural network. J. Intell. Robot. Syst. 40(2), 113–145 (2004)

    Article  Google Scholar 

  3. Song, Q., Hu, W.J., Soh, Y.C.: Robust adaptive dead zone technology for fault-tolerant control of robot manipulators using neural networks. J. Intell. Robot. Syst. 33(2), 113–137 (2002)

    Article  MATH  Google Scholar 

  4. Blanke, M.: Fault-tolerant control systems – a history view. Control Eng. Pract. 5(5), 693–702 (1997)

    Article  MathSciNet  Google Scholar 

  5. Lewis, F.L., Liu, K., Yesildirek, A.: Neural net robot controller with guaranteed tracking performance. IEEE Trans. Neural Netw. 6(3), 703–715 (1995)

    Article  Google Scholar 

  6. Gu, D.B., Hu, H.S.: Neural predictive control for a car-like mobile robot. Robot. Auton. Syst. 39(2), 73–86 (2002)

    Article  Google Scholar 

  7. Liu, Z., Li, C.W.: Fuzzy neural network quadratic stabilization output feedback control for biped robots via H infinity approach. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 33(1), 67–84 (2003)

    Article  Google Scholar 

  8. Rupp, M., Sayed, A.H.: Supervised learning of perceptron and output feedback dynamic networks: a feedback analysis via the small gain theorem. IEEE Trans. Neural Netw. 8(3), 612–622 (1997)

    Article  Google Scholar 

  9. Mak, M.W., Ku, K.W., Lu, Y.L.: On the improvement of the real time recurrent learning algorithm for recurrent neural networks. Neurocomputing 24(1), 13–36 (1999)

    Article  MATH  Google Scholar 

  10. Haykin, S.: Neural Networks. Prentice-Hall, Upper saddle River, NJ (1999)

    MATH  Google Scholar 

  11. Atiya, A.F., Parlos, A.G.: New results on recurrent network training: unifying the algorithms and accelerating convergence. IEEE Trans. Neural Netw. 11(3), 697–709 (2000)

    Article  Google Scholar 

  12. Mandic, D.P., Chambers, J.A.: Recurrent Neural Networks for Prediction: Learning Algorithms, Architecture and Stability. Wiley, Singapore (2001)

    Google Scholar 

  13. Sun, F.C., Sun, Z.Q., Woo, P.Y.: Neural network-based adaptive controller design of robotic manipulators with an observer. IEEE Trans. Neural Netw. 12(1), 54–67 (2001)

    Article  Google Scholar 

  14. Song, Q., Xiao, J., Soh, Y.C.: Robust back-propagation training algorithm for multi-layered neural tracking controller. IEEE Trans. Neural Netw. 10(5), 1133–1141 (1999)

    Article  Google Scholar 

  15. Lin, C.M., Chen, C.H., Hus, C.F., Fan, W.Z.: Robust fault-tolerant control for robotic system using recurrent cerebellar model articulation controller. IEEE Int. Conf. Industrial Technology 7, 1006–1011 (2005)

    Article  Google Scholar 

  16. Song, Q., Grimble, M.J.: Robust neural network/proportional tracking controller with guaranteed global stability. Proc. Int. Symp. Intelligent Control 15, 34–39 (2003)

    Google Scholar 

  17. Lewis, F.L.: Control of Robot Manipulators. Macmillan, New York (1993)

    Google Scholar 

  18. Tzafestas, S.G., Krikochoritis, A.E., Tzafestas, C.S.: A robust-adaptive locomotion controller for 9-link with rapidly varying unkown parameters. Proc. Mediterranean Conf. Control and Systems 5, 21–23 (1997)

    Google Scholar 

  19. Cluett, W.R., Shah, L., Fisher, D.G.: Robustness analysis of discrete-time adaptive control systems using input-output stability theory: a tutorial. IEE Proc. Part D. 135(2), 133–141 (1988)

    MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore

    Wu Yilei, Song Qing & Yang Xulei

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  1. Wu Yilei
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  2. Song Qing
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  3. Yang Xulei
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Correspondence to Wu Yilei.

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Wu, Y., Song, Q. & Yang, X. Robust Recurrent Neural Network Control of Biped Robot. J Intell Robot Syst 49, 151–169 (2007). https://doi.org/10.1007/s10846-007-9133-1

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  • DOI: https://doi.org/10.1007/s10846-007-9133-1

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