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A study of the use of fuzzy control theory to stabilize the gait of biped robots

Published online by Cambridge University Press:  24 July 2014

Hai-Wu Lee*
Affiliation:
Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan, Republic of China
*
*Corresponding author. E-mail: [email protected]

Summary

This paper designs a biped robot to perform appropriate walking exercises according to the terrain, which then walks stably on a flat environment. The concept of fuzzy logic is combined with the Linear Quadratic Regulator (LQR) controller theory to design the best method to allow the biped robot system to have a balanced and stable gait. Traditional controllers are designed using mathematical models of physical systems, but a fuzzy controller is a physical system that uses an inexact mathematical model, which involves sets and membership functions. Fuzzy controllers use fuzzification, fuzzy control rules, and defuzzification. The method and theory of control: A stable gait for robots is achieved using inverse kinematics, fuzzy concepts, the LQR controller theory, path design, and the characteristics of a dynamic equation. It is then simulated using mathematical tools to prove that the system eliminates swinging by biped robots without fuzzy control knowing beforehand the dynamic model the system is using. Proportional-Integral-Differential control achieves a stable gait design in a flat environment.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

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References

1.Yamaguchi, J., Kinoshita, N., Takanishi, A. and Kato, I., “Development of a Dynamic Biped Walking System for Humanoid Development of a Biped Walking Robot Adapting to the Humans' Living Floor,” Proceedings of the 1996 IEEE International Conference on Robotics and Automation, Minneapolis, MN, Vol. 1 (Apr. 22–28, 1996) pp. 232–239.Google Scholar
2.Zheng, Y. F. and Shen, J., “Gait synthesis for the SD-2 biped robot to climb sloping surface,” IEEE Trans. Robot. Autom. 6, 8696 (Feb. 1990).Google Scholar
3.Shuta, M., Eiji, Y. and Kouji, F., “Fuzzy Control of Dynamic Biped Walking Robot,” In: IEEE International Conference on Fuzzy Systems, Yokohama, vol. 1 (Mar. 20–24, 1995) pp. 77–82.Google Scholar
4.Shuuji, K. and Kazuo, T., “Experimental Study of Biped Dynamic Walking in the Linear Inverted Pendulum Mode,” Proceedings of the IEEE International Conference on Robotics and Automation (1995) pp. 2885–2891.Google Scholar
5.Roussel, L., Canudas-de-Wit, C. and Goswami, A., “Generation of Energy Optimal Complete Gait Cycles for Biped Robots,” Proceedings IEEE International Conference Robotics and Automation (1998).Google Scholar
6.Geizzle, J.W., “An Analytical Approach to Asymptotically Stable Walking in Planar Biped Robots,” SuperMechano Systems Symposium, Tokyo, Japan (Nov. 19–20, 2001).Google Scholar
7.Lum, H. K., Zribi, M. and Soh, Y. C., “Planning and control of a biped robot,” Int. J. Eng. Sci. 37, 13191349 (1999).Google Scholar
8.Hirai, K., Hirose, M., Haikawa, Y. and Takenaka, T., “The Development of Honda Humanoid Robot,” Proceedings of IEEE International Conference on Robotics and Automation (1998).Google Scholar
9.Spong, M. W., Hutchinson, S. and Vidyasagar, M., Robot Modeling and Control (Wiley, Hoboken, NJ, 2006).Google Scholar
10.Craig, J. J., Introduction to Robotics Mechanics and Control, 3rd ed. (Prentice Hall, Upper Saddle River, NJ, 2005).Google Scholar
11.Wang, L. X., A Course in Fuzzy System and Control (Prentice Hall, Englewood Cliffs, NJ, 1997).Google Scholar
12.Wang, L. X., Adaptive Fuzzy System and Control: Design and Stability Analysis (Prentice Hall, Englewood Cliffs, NJ, 1994).Google Scholar
13.Zhou, C. and Jagannathan, K., “Adaptive network based fuzzy control of a dynamic biped walking robot,” IEEE international joint Symposia on Intelligence and Systems, Rockville, MD, (Nov. 4–5, 1996), pp. 109–116.Google Scholar
14.Li, T. H. S., Su, Y. T., Liu, S. H., Hu, J. J. and Chen, C. C., “Dynamic balance control for biped robot walking using sensor fusion, Kalman filter, and fuzzy logic,” IEEE Trans. Indust. Electron. 59 (11), 43944408 (2012).Google Scholar
15.Li, T. H. S., Su, Y. T, Lai, S. W. and Hu, J. J, “Walking motion generation, synthesis, and control for biped robot by using PGRL, LPI, and fuzzy logic,” IEEE Trans. Syst. Man Cybern. B 41 (3), 736748 (2011).Google Scholar
16.Li, W. and Feng, X., “Behavior Fusion for Robot Navigation in Uncertain Environments Using Fuzzy Logic,” Proceedings of the 1994 IEEE International Conference on Systems, Man, and Cybernetics, Humans, Information and Technology, San Antonio, TX, Vol. 2. (Oct 2–5, 1994) pp. 1790–1796.Google Scholar
17.Yu, S. J., “Application an Iterative Learning – Sliding Mode Control to Inverse pendulum system,” Proceedings of the 3rd International Conference on Machine and Cybernetics (2004) pp. 26–29.Google Scholar
18.Phuong, N. T., Kim, D. W., Kim, H. K. and Kim, S. B., “An Optimal Control Method for Biped Robot with Stable Walking Gait,” Proceedings of the 8th IEEE-RAS International Conference on Humanoid Robots, Daejeon, Korea (Dec. 1–3, 2008).Google Scholar
19.Guo, Z., “A Gain-Scheduling Optimal Fuzzy Logic Control Design for Unicycle,” Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Suntec Convention and Exhibition Center (2009).Google Scholar
20.Geng, T. and Gan, T. Q., “Planar biped walking with an equilibrium point controller and state machines,” IEEE/ASME Trans. Mechatronics 15 (2) (2010) pp. 253260.Google Scholar