Robustness of time-scale learning of robot motions to uncertainty in acquired knowledge

A disadvantage of present iterative learning control algorithms is that they are generally applicable only in cases where a certain task is performed over and over again. Consequently, if knowledge or control inputs acquired from learning a task can be used on similar tasks, learning will be more efficient. Recently, several methods for constructing the control input of a new motion based on the control inputs acquired from previous learning of similar tasks have been proposed. However, these methods assumed that the perfect control inputs could be obtained from the previous learning. In practice, the control inputs could never be obtained exactly from learning in the presence of certain uncertainties such as disturbance and measurement noises. In addition, it is also not known for sure how the basic motion patterns should be chosen for learning. In this article, the robustness problem of the time-scale learning control to uncertainty in the acquired learning control inputs is formulated and solved. From the analysis, certain new insights such as its implication to choices of basic motion I patterns for time-scale learning will be discussed. Simulation results of a 3-link robot are presented to illustrate the analysis. (C) 2001 John Wiley & Sons, Inc.