JRM Vol.16 No.1 pp. 54-60
doi: 10.20965/jrm.2004.p0054


Expert Skill-Based Gain Tuning in Discrete-Time Adaptive Control for Robots

Haruhisa Kawasaki, and Geng Li

Faculty of Engineering, Gifu University, 1-1 Yanagito, Gifu 501-1193, Japan

October 10, 2003
January 15, 2004
February 20, 2004
robot, adaptive control, discrete time system, gain tuning, sampling period, Lyapunov function, stability
This paper presents a gain tuning method based on the sampling period in discrete-time adaptive control for robots. Gain matrices of model-based adaptive control in a continuous-time system are allowed a high gain positive definite. The maximum of the gains depends on the sampling period, however, and gain tuning is very time-consuming. It is thus desirable to give a gain tuning rule in discrete-time adaptive control. The proposed gain tuning consists of two steps. The first is gain tuning at the basic sampling period by a skilful specialist by trial and error. The second step, executed if the sampling period changes, is a new gain calculation based on a new sampling period. The simulation and experiments with 1-dof and 3-dof robots demonstrate that the robot controller is stable at the large variance of sampling period changes and more accurate than a fixed gain controller.
Cite this article as:
H. Kawasaki and G. Li, “Expert Skill-Based Gain Tuning in Discrete-Time Adaptive Control for Robots,” J. Robot. Mechatron., Vol.16 No.1, pp. 54-60, 2004.
Data files:
  1. [1] N. Sadegh and R. Horowitz, “Stability Analysis of an Adaptive Controller for Robotics Manipulators,” Proc. of the 26th. Conf. on Decision and Control, pp. 1223-1229, 1987.
  2. [2] J. J. E. Slotine and W. Li, “On the Adaptive Control of Robot Manipulators,” The Int. Jour. of Robotics Research, Vol.6, No.3, pp. 50-59, 1987.
  3. [3] D. E. Koditschek, “Adaptive Techniques for Mechanical Systems,” Proc. of the Fifth Yale Workshop on the Applications of Adaptive Systems Theory, pp. 259-265, 1987.
  4. [4] H. Berghuis, R. Ortega and H. Nijmeijer, “A Robust Adaptive Controller for Robot Manipulators,” IEEE Int. Conf. on Robotics Automat., pp. 1876-1881,1992.
  5. [5] R. R. Y. Zhen, “An Adaptive Approach to Constrained Robot Motion Control, IEEE lnt. Conf. on Robotics and Automation,” pp. 1833-1838, 1995.
  6. [6] G. L. Showman and M. B. Leahy Jr., “Apprication of Principal Base Parameter Analysys to Design of Adaptive Robot Controllers,” Proc. of IEEE Int. Conf. of Robotics and Automation, pp. 1889-1894, 1992.
  7. [7] H. Kawasaki and Y. Oooka, “Adaptive Control in Task Space for Constrained Robot,” Proc. of 4th ECPD, pp. 78-83, 1998.
  8. [8] H. Kawasaki, T. Bito and K. Kanzaki, “An Efficient Algorithm for Model-Based Adaptive Control of Robotic anipulators,” IEEE Trans. on Robotics and Automation, 12-3, pp. 496-501, 1996.
  9. [9] Cuadrado, J. Cardenal and E. Bayo, “Modeling and solution for efficient real-time simulation of multibody dynamics,” Multibody System Dynamics, 1, pp. 259-280, 1997.
  10. [10] G. Li and H. Kawasaki, “Automatic PID Tuning Considering Motor Maximum Input and Power Consumption for Servo Control System,” Transaction of JSME, Vol. 69, No. 682-C, pp. 1556-1562. (In Japanese)

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 05, 2024