Trajectory Optimization and NMPC Tracking for a Fixed Wing UAV in Deep Stall with Perch Landing

TL;DR

This paper introduces a novel fixed-wing UAV recovery method combining deep stall trajectory planning and NMPC tracking to enable quick, controlled landings on a recovery net, even under wind disturbances.

Contribution

It proposes a new trajectory generation method involving deep stall and perching, along with an NMPC controller with terminal constraints for robust tracking.

Findings

Reduces landing time compared to traditional net recovery.

Achieves lower final airspeed for safer landings.

Demonstrates effectiveness under various wind conditions.

Abstract

This paper presents a novel recovery technique for a fixed-wing UAV (Unmanned Aerial Vehicle) based on constrained optimization: i) we propose a trajectory generation for landing the UAV where it first reduces its altitude by deep stalling, then perches on a recovery net, ii) we design an NMPC (Nonlinear Model Predictive Control) tracking controller with terminal constraints for the optimal generated trajectory under disturbances. Compared to nominal net recovery procedures, this technique greatly reduces the landing time and the final airspeed of the UAV. Simulation results for various wind conditions demonstrate the feasibility of the idea.