Smoother-Based Iterative Learning Control for UAV Trajectory Tracking

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 performance of system executes same task multiple times can be improved by learning from previous executions. While we will specifically refer multirotor platforms for experimental validation, formulation applied any dynamic (including systems underlying feedback loops). The novelty this work is introduction smoother estimate repetitive disturbance improve performance. estimator must rely accurate model has been obtained through black-box identification procedure using Predictor-Based Subspace Identification (PBSID) algorithm. A Monte Carlo analysis carried out aim showing improvements and limitations proposed respect approaches. Finally, validated activities involving small quadrotor performing aggressive manoeuver.