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A Study on Intelligent Control of Real-Time Working Motion Generation of Bipped Robot
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 Title & Authors
A Study on Intelligent Control of Real-Time Working Motion Generation of Bipped Robot
Kim, Min-Seong; Jo, Sang-Young; Koo, Young-Mok; Jeong, Yang-Gun; Han, Sung-Hyun;
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 Abstract
In this paper, we propose a new learning control scheme for various walk motion control of biped robot with same learning-base by neural network. We show that learning control algorithm based on the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multi layer back propagation neural network identification is simulated to obtain a dynamic model of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The biped robots have been received increased attention due to several properties such as its human like mobility and the high-order dynamic equation. These properties enable the biped robots to perform the dangerous works instead of human beings. Thus, the stable walking control of the biped robots is a fundamentally hot issue and has been studied by many researchers. However, legged locomotion, it is difficult to control the biped robots. Besides, unlike the robot manipulator, the biped robot has an uncontrollable degree of freedom playing a dominant role for the stability of their locomotion in the biped robot dynamics. From the simulation and experiments the reliability of iterative learning control was illustrated.
 Keywords
Learning control;Biped Robot;Neural Network;Real-Time;
 Language
Korean
 Cited by
 References
1.
Y. F. Zheng and F. R. Sias Jr., Design and motion control of practical biped robots,Int. J. on R & A, Int. J. on R & A, vol.3, No. 2, pp. 70-77, China. 1988

2.
Sylvain Miossec, Yannick Aoustin, A Simplified Stability Study for a Biped Walk with Underactuated and Overactuated Phases, The International Journal of Robotics Reserch, vol 24, no. 7, pp. 537-551,France. 2005, crossref(new window)

3.
Tang, Z., Zhou, C. and Sun, Z., Trajectory Planning for Smooth Transition of a Biped Robot Proc Int. conf. on Robotics & Automation, pp. 2455-2460, taiwan. 2003,

4.
M. Vukobratovic, A. A. Frank, and D. Juricic, On the Stability of BipedLocomotion, Proc. IEEE Transactions on Biomedical Engineering, Vol. BME-17, No.1, pp. 25-36, Yugoslavia. 1970,

5.
R. B. McGhee, Finite State Control of Quadruped Locomotion, Processing of Second International Symposium on External Control of Human Extremities, Dubrovnik, 1966,

6.
I. Kato and H. Tsuiki, Hydraulically Powered Biped Walking Machine with a High Carrying Capacity, Processing of Fourth International Symposium on External Control of Human Extremities, Dubrovnik, Yugoslavia. 1972,.

7.
D. Sbarbaro and K. J. Hunt, Neural networks for Nonlinear Internal Model Control, IEEE Processing-D, vol. 138, no. 5, pp431-438, Scotland. 1991, crossref(new window)

8.
L. Kraft and P. Campagna, A Comparision Between CMAC Neural Network Control and Two Traditional Adaptive Control Systems, IEEE Control System Magazine, pp. 36-43, USA 1990,

9.
D. I. Jones, P. J. Fleming and A. E. B. Ruano, Connectionist Approach to PID Autotuning, IEE Processing-D, vol. 139, no. 3, pp. 279-285, UK. 1992, crossref(new window)

10.
R. S. Sutton and A. G. Barto, Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems, IEEE Transactions on System, Man and Cybernetics, vol. smc-13, no. 5, PP.834-846, USA. 1983, crossref(new window)

11.
Hirai, K., et al, The development of Honda humanoid robot, Proceedings of ICRA 2:1321-1326, Japan. 1998,

12.
Chang Seok Oh, Neuro Computer, Naeha All Rights Reserved, Korea. 2000,

13.
Qiang Huang, Kazuhito Yokoi, Planning Walking Patterns for a Biped Robot, IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, China. 2001,

14.
Seong-Su Lee, Yong-Wook Kim, Hun Oh, Wal-Seo Park, Implementation of Self-adaptive System using the Algorithm of Neural Network Learning Gain, International Journal of Control, Automation, and Systems, vol.6, no.3, pp. 453-459, Korea. 2008.