Iterative Learning Control Analysis, Design, and Experiments

In many industrial robot applications it is a fact that the robot is programmed to do the same task repeatedly. By observing the control error in the different iterations of the same task it becomes clear that it is actually highly repetitive. Iterative Learning Control (ILC) allows to iteratively compensate for and, hence, remove this repetitive error. In the thesis different aspects of iterative learning control are covered. Although stability is the most important in practice the design aspect is also highlighted. Several design schemes for iterative learning control methods are presented, including first order as well as second order iterative learning control. An adaptive approach to iterative learning control is also discussed. Many of the suggested design methods are also given with stability and robustness results. The application, industrial robot control, that has been used as a testbed throughout the thesis is described. The description includes a general discussion on robot modeling and control as well as a specific discussion on the implementation of the functions needed in the commercial robot control software in order to make it possible to apply iterative learning control. The suggested iterative learning control design methods are all tested on the robot. Some practical aspects on the path following problem for industrial robots using iterative learning control are discussed. A potential solution to the path tracking problem using additional sensors is given, although it is not yet implemented on the robot.