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

TL;DR

This paper proposes a trajectory generation for landing the UAV where it reduces its altitude by deep stalling, then perches on a recovery net, and designs an NMPC (Nonlinear Model Predictive Control) tracking controller with terminal constraints for the optimal generated trajectory under disturbances.

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.

Figures (13)

• Figure 1: Aerodynamic forces and moment acting on a longitudinal fixed-wing UAV
• Figure 2: $\mathcal{X}_{g_1}$, $\mathcal{X}_{g_2}$, and $\mathcal{C}_{\text{pos}}$ projected to the $xz$ plane.
• Figure 3: $\mathcal{X}_{g_1}$, $\mathcal{X}_{g_2}$, and $\mathcal{C}_{V_a}$ projected to the $x V_a$ plane.
• Figure 4: Aerodynamic coefficients.
• Figure 5: Gust in the body frame for the $90$ tracking cases.