Iterative Learning Control Based Robust Distributed Algorithm for Non-Holonomic Mobile Robots Formation

This paper presents an iterative learning control-based robust distributed algorithm on the formation issue for a group of differential-drive mobile robots. The fundamental robustness problem in practical application involving initial state shifts, disturbances, noises, and communication time-delays are considered. The distributed algorithm is proposed for each robot in directed network, which is based on the iterative learning rule with both predictive and current learning terms. It is shown that the convergence of formation tracking objective can be guaranteed under a matrix norm condition by using the two-dimensional analysis approach. Numerical simulation and experiment are both given to validate the effectiveness of the proposed algorithm.