An iterative learning controller for nonholonomic robots

We present an iterative learning controller for nonholonomic systems in chained form. The learning algorithm relies on the fact that chained-form systems are linear under piecewise-constant inputs. The proposed control scheme requires the execution of a small number of experiments in order to drive the system to the desired state in finite time, with nice convergence and robustness properties with respect to modeling inaccuracies as well as disturbances. As a case study, a car-like wheeled mobile robot is considered. Both simulation and experimental results are reported in order to show the performance of the proposed method