Cyclic Learning Control of Chained-form Systems with Application to Car-like Robots

An iterative learning controller for steering chained-form systems is presented. The learning algorithm relies on chained-form systems being linear under piecewise-constant inputs. The proposed control scheme requires the execution of a limited number of experiments in order to reach the desired state in finite time, with nice convergence and robustness properties with respect to modeling inaccuracies and disturbances. To overcome the necessity of exact system re-initialization at each experiment, the basic method is modified so as to obtain a cyclic controller, in which the system is cyclically steered among an arbitrary number of states. As a case study, a car-like robot is considered.