Smoother-Based Iterative Learning Control for UAV Trajectory Tracking

Abstract

This letter presents a data-based control approach to achieve high-performance trajectory tracking with Unmanned Aerial Vehicles (UAVs). We revisit an existing Iterative Learning Control (ILC) algorithm based on the notion that the performance of a system that executes the same task multiple times can be improved by learning from previous executions. While we will specifically refer to multirotor platforms for the experimental validation, the formulation can be applied to any dynamic system (including systems with underlying feedback loops). The novelty of this work is the introduction of a smoother to estimate the repetitive disturbance to improve the learning performance. This estimator must rely on an accurate system model that has been obtained through a black-box identification procedure using the Predictor-Based Subspace Identification (PBSID) algorithm. A Monte Carlo analysis has been carried out with the aim of showing the performance improvements and limitations of the proposed algorithm with respect to existing approaches. Finally, the proposed approach has been validated through experimental activities involving a small quadrotor performing an aggressive manoeuver.