GP-based NMPC for Aerial Transportation of Suspended Loads

In this work, we leverage Gaussian Processes (GPs) and present a learning-based control scheme for the transportation of cable-suspended loads with multirotor Unmanned Aerial Vehicles (UAVs). Our ultimate goal is to approximate the model discrepancies that exist between the actual and nominal system dynamics. Towards this direction, weighted and sparse Gaussian Process (GP) regression is exploited so as to approximate online the model errors and guarantee real-time performance while also ensuring adaptability to the conditions prevailing in the outdoor environment where the UAV is deployed. The learned model errors are fed into a nonlinear Model Predictive Controller (NMPC), formulated for the corrected system dynamics, which achieves the transportation of the UAV towards reference positions with simultaneous minimization of the cable angular motion, regardless of the outdoor conditions and the existence of external disturbances, primarily stemming from the unknown wind. The proposed scheme is validated through simulations and real-world experiments with an octorotor, demonstrating an 80% reduction in the steady-state position error under 4 Beaufort wind conditions compared to the nominal NMPC.