Tracking Trajectory Using Iterative Learning Control

In this paper, Iterative Learning Control approach for tracking trajectory is presented. The paper includes a general introduction to ILC and a technical description of the methodology. Iterative learning control (ILC) is a learning technique used to improve the performance of systems that execute the same task multiple times. Learning transient behavior has emerged as an important topic in the design and analysis of ILC systems. Given a desired trajectory to be followed, the proposed learning algorithm improves the system performance from trail to trail by exploiting the experience gained from previous repetitions. Taking advantage of the a-priori knowledge about the systems dominating dynamics, a data-based update rule is derived which adapts the feedforward input signal after each trial. Different (nonlinear) performance objectives can be specified defining the overall learning behavior. Finally, the proposed algorithm is successfully applied to improve system performance and also to track exactly the given trajectory.