Adaptive Iterative Learning Control for Multiple Autonomous Vehicles with Input Constraints

This paper addresses the consensus tracking control problem for multiple autonomous vehicles (MAVs) under the influence of input constraints and system uncertainties. To tackle the system uncertainties, we reformulate the MAVs dynamics into a parametric form, aggregating the uncertainties into an unknown vector that is estimated using an iterative learning scheme. In addition, a saturation compensator is designed to compensate the adverse impacts of the input saturation. Based on this, a novel adaptive iterative learning control (AILC) scheme is then developed to achieve the consensus tracking assignment. The analysis of the convergence for the proposed control algorithm is conducted through the composite energy function approach, and its effectiveness is validated via numerical simulations.