Data-driven optimal shared control of unmanned aerial vehicles

Cooperation between humans and autonomy is a critical topic of unmanned aerial vehicle (UAV) control. How to co-pilot the UAV with human operator to achieve optimal performance presents a significant challenge. In this paper, we propose a novel data-driven optimal shared control method for UAV using the Koopman operators to predict the nonlinear dynamics of the UAVs. An original shared control mechanism is established to allocate the relationship between optimal and human control inputs. The model of the system is learned from human maneuver data via the Koopman operator approach, and the optimal controller is approximated online using reinforcement learning techniques. The Lyapunov theory analyzes the stability of the proposed method. Compared with offline RL methods, the proposed method can learn the optimal controller online without a precise UAV dynamics model from human maneuver data. The effectiveness of the proposed method is demonstrated by numerical and Human-in-the-loop (HiTL) simulation.