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 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.