Trajectory tracking and tether tension control of tethered aerial vehicle using Nonlinear Model Predictive Control

Tethered unmanned aerial vehicles are an effective solution for applications demanding extended flight durations. However, these systems exhibit complex nonlinear dynamics and coupling effects, which are further amplified in scenarios where the ground component of the system — the Launch and Recovery System (LARS) is mounted on a mobile platform. In this study, the UAV has the task of following a user-defined trajectory while keeping the tether interaction force and the tether length within safe operating limits. This results in a nonlinear control problem that is subject to constraints. To solve this problem, a Nonlinear Model Predictive Control (NMPC) for the tethered aerial system is proposed in this paper. State estimation is achieved by capturing the tether interaction force using a minimal proprioceptive sensing system. It is further demonstrated that active tether force control can improve estimation accuracy. Finally, the proposed control and estimation strategies are implemented and validated experimentally on a UAV-LARS system.