A new algorithm of adaptive iterative learning control for uncertain robotic systems

In this paper, we propose a new adaptive iterative learning control (AILC) scheme for a class of parametric uncertain robotic systems with disturbances. The main feature of the proposed AILC scheme is that all the estimated parameters are updated by a new adaptive law which combines time-domain and...

Full description

Saved in:
Bibliographic Details
Published in2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422) Vol. 3; pp. 4130 - 4135 vol.3
Main Authors Chun-Te Hsu, Chiang-Ju Chien, Chia-Yu Yao
Format Conference Proceeding
LanguageEnglish
Published IEEE 2003
Subjects
Adaptive control
Control systems
Convergence
Iterative algorithms
Parameter estimation
Programmable control
Robot control
Signal analysis
Time domain analysis
Upper bound
Online AccessGet full text

Cover

More Information
Summary:In this paper, we propose a new adaptive iterative learning control (AILC) scheme for a class of parametric uncertain robotic systems with disturbances. The main feature of the proposed AILC scheme is that all the estimated parameters are updated by a new adaptive law which combines time-domain and iteration-domain adaptation. This new adaptive law is designed without using projection or deadzone mechanism and can be applied to system with non-periodic or non-repeatable disturbance. Via a rigorous technical analysis, it is shown that all adjustable parameters as well as the internal signals remain bounded in the time-domain for each iteration and the tracking error can be driven to zero in the iteration-domain. Finally, the learning performance will be demonstrated by a simulation example.
ISBN:0780377362
9780780377363
ISSN:1050-4729
2577-087X
DOI:10.1109/ROBOT.2003.1242232