A simple structure iterative learning algorithm experimentally validated for mobile robot path-tracking

The general subject area of this paper is iterative learning control based on a linear model of the dynamics using the theory of discrete linear repetitive processes for analysis. Linear matrix inequality based computations are developed to compute the stabilizing feedback controller in the time domain and a feedforward controller that guarantees convergence in the trial-to-trial domain. Use of the generalized Kalman-Yakubovich-Popov lemma allows a direct treatment of finite frequency range performance specifications. Since weighting filters are not used, an unnecessary increase in the controller order is avoided. To illustrate the performance of the new design, the problem of path-tracking control for wheeled mobile robot is considered. Experimental verification results using a Lego EV3-based mobile robot are also given, including a comparison of trajectory tracking performance with an H
_∞
based design.