Skip to main content Accessibility help

Locomotion control of a hydraulically actuated hexapod robot by robust adaptive fuzzy control and dead-zone compensation

  • Ranjit Kumar Barai (a1) and Kenzo Nonami (a2)


This investigation presents locomotion control of a hydraulically actuated six-legged humanitarian demining robot by robust adaptive fuzzy control in conjunction with the dead zone compensation technique within independent joint control framework. For proper locomotion of the demining robot, accurate tracking of the desired joint trajectory is very important. However, high degree of nonlinearity, the uncertainties due to changing hydraulic properties, and delay due to the flow of oil and dead zone of the proportional electromagnetic control valve results into an inaccurate plant model for the hydraulically actuated robotic joints. Consequently, model-based classical control techniques result into a large tracking error. Therefore, adaptive fuzzy control technique, being a model independent control paradigm for complex and uncertain systems, is a good choice for such systems. In this work, a hydraulic dead zone compensated robust adaptive fuzzy control law has been proposed for locomotion control of hydraulically actuated hexapod demining robot. The experimental results exhibit a fairly accurate trajectory tracking of the leg joints and, consequently, very stable locomotion of the walking robot.


Corresponding author

*Corresponding author. E-mail:


Hide All
1.Ioannou, P. A., “Robust adaptive control: A unified approach,” Proc. IEEE 79 (12), 17361767 1991.
2.Wang, L. X., “Stable adaptive fuzzy control of nonlinear systems,” IEEE Trans. Fuzzy Syst. 1 (2), 146155 1993.
3.Chen, B. S., Lee, C. H. and Chang, Y. C., “H∞ tracking design of uncertain nonlinear SISO systems: Adaptive fuzzy approach,” IEEE Trans. Fuzzy Syst. 4 (1), 3243 1996.
4.Park, J. H., Seo, S. J. and Park, G. T., “Robust adaptive fuzzy controller for nonlinear system using estimation of bounds for approximation errors,” Fuzzy Sets Syst. 133, 1936 2003.
5.Bu, F. and Yao, B., “Nonlinear model based coordinated adaptive robust control of electro-hydraulic arms via overparameterizing method,” Proceedings of the IEEE International Conference on Robotics and Automation 2001 pp. 3459–3464.
6.Wang, L. X. and Mendel, J. M., “Fuzzy basis functions, universal approximation, and orthogonal least-square learning,” IEEE Trans. Neural Netw. 3 (5), 807814 1992.
7.Mudi, R. K. and Pal, N. R., “A self-tuning fuzzy PI-controller,” Fuzzy Sets Syst. 115, 327338 2000.
8.Nonami, K., Huang, Q., Komizo, D., Fukao, Y., Asai, Y., Shiraishi, Y., Fujimoto, M. and Ikedo, Y., “Development and control of mine detection robot COMET-II and COMET-III,” JSME Int. J. Ser. C 46 (3), 881890 2003.
9.Driankov, D., Hellendoorn, H. and Reinfrank, M., An Introduction to Fuzzy Control (Springer-Verlag, Berlin, Germany, 1996).
10.Fischle, K. and Schröder, D., “An improved stable adaptive fuzzy control method,” IEEE Trans. Fuzzy Syst. 7 (1), 2740 1999.
11.Ge, S. S. and Wang, J., “Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems,” IEEE Trans. Neural Netw. 13 (6), 14091419 2002.
12.Kim, Y. T. and Bien, Z. Z., “Robust adaptive fuzzy control in the presence of external disturbance and approximation error,” Fuzzy Sets Syst. 148, 377393 2004.
13.Corbet, T., Sepehri, N. and Lawrence, P. D., “Fuzzy control of a class of hydraulically actuated industrial robots,” IEEE Trans. Control Syst. Technol. 4 (4), 419426 1996.
14.Wang, H. O. and Tanaka, K., “An approach to fuzzy control of nonlinear systems: Stability and design issues,” IEEE Trans. Fuzzy Syst. 4 (1), 1423 1996.
15.Tanaka, K. and Sugeno, M., “Stability and design of fuzzy control systems,” Fuzzy Sets Syst. 45, 135156 1992.
16.Spong, M. W. and Vidyasagar, M., Robot Dynamics and Control (Wiley, New York, NY, 1989).
17.Ioannou, P. A. and Sung, J., Robust Adaptive Control (Prentice-Hall, Upper Saddle River, NJ, 1996).
18.Nonami, K. and Ikedo, Y., “Walking control of COMET-III using discrete time preview sliding mode control,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems 2004 pp. 3219–3225.
19.Corbet, T., Sepehri, N. and Lawrence, P. D., “Fuzzy control of a class of hydraulically actuated industrial robot,” IEEE Trans. Control Syst. Technol. 4 (4), 419426 1996.
20.Wang, X. S., Su, C. Y. and Hong, H., “Robust adaptive control of a class of nonlinear systems with unknown dead-zone,” Proceedings of the 40th IEEE Conference on Decision and Control 2001 pp. 1627–1632.
21.Johnson, C. R., “Adaptive implementation of one-step-ahead optimal control via input matching,” IEEE Trans. Autom. ControlAC-23, 865–872 1978.
22.Slotine, J. and Li, W., Applied Nonlinear Control (Prentice-Hall, Englewood Cliffs, NJ, 1991).


Related content

Powered by UNSILO

Locomotion control of a hydraulically actuated hexapod robot by robust adaptive fuzzy control and dead-zone compensation

  • Ranjit Kumar Barai (a1) and Kenzo Nonami (a2)


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.