An Iterative Learning Controller for Nonholonomic Mobile Robots

We present an iterative learning controller that applies to nonholonomic mobile robots as well as to other systems which can be put in chained form. The learning algorithm exploites the fact that chained-form systems are linear under piecewiseconstant 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. To avoid the necessity of exactly re-initializing the system at each iteration, the basic method is modified so as to obtain a cyclic controller, by which the system is cyclically steered among an arbitrary sequence of states. As a case study, a car-like mobile robot is considered. Both simulation and experimental results are reported in order to show the performance of the method.