An Optimization-Based Iterative Learning Control Design Method for UAV’s Trajectory Tracking

Abstract

This paper presents an iterative learning control (ILC) design method to improve the unmanned aerial vehicle’s (UAV’s) tracking performance. ILC is a feedforward control method that aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning module, which includes a learning filter and a robust filter, to generate the learning signal and to improve the tracking performance of the current iteration. This paper presents a two-step optimization based design method for these filters. As to the learning filter design, we transform it into a feedback controller design problem for a purposely constructed system. The formulated controller design problem is solved based on H-infinity optimal control theory. After the learning filter is designed, the robust filter is obtained by solving an additional H-infinity optimization problem. Through the proposed two-step optimization-based filter design method, the system’s stability is guaranteed and the learning performance is optimized. The proposed filter design method and the regarding ILC algorithm are applied to the UAV’s trajectory tracking system and are validated by numerical studies based on Gazebo, one high-fidelity simulation platform for UAVs.