Planning and Control for Autonomous Landing of Quadrotor Unmanned Aerial Vehicles With Disturbances

Abstract

Quadrotor unmanned aerial vehicles (UAVs) have advantages such as simple structure, flexible manipulation, and strong capabilities of vertical takeoff/landing and hovering, thus being widely applied in various complex environments and tasks. However, their short endurance limits the scope of application. The collaboration between aerial UAVs and ground robots has become a viable alternative, among which the autonomous and precise landing of UAVs on mobile platforms is a key technology for air‐ground collaborative tasks. This paper proposes a trajectory planning method for autonomous landing and designs a robust nonlinear model predictive controller (NMPC) for wind‐disturbed environments. For the landing trajectory planning, an efficient trajectory planning method based on MINCO is proposed, which transforms the trajectory planning problem into an optimization problem. For the wind‐disturbed environments, extended state observers are introduced to realize real‐time observation and estimation of the disturbances suffered by the UAV, and the estimated values are fed back to the prediction model of the NMPC controller for correction. Eventually, the effectiveness of the proposed algorithm is verified through simulations and physical experiments.