Autonomous helicopter aerial refueling: controller design and performance guarantees

TL;DR

Autonomous helicopter aerial refueling is challenged by wake interactions, high-speed docking, and the probe’s offset from the CG. The authors develop a nonlinear dynamic-inversion outer-loop controller that explicitly accounts for the probe orientation and probe-state feedback, and they prove ultimate boundedness of the docking error via a Lyapunov function $V(E)$, yielding an invariant set $\Lambda$ whose size depends on $K_P$, $K_D$, and disturbance bounds $\delta_D$, $\delta_R$. The approach is validated on a high-fidelity UH60 simulator with realistic drogue dynamics and wind, achieving a mean docking error of $0.14$ m and a docking success rate of $74\%$, a $36\%$ improvement over a standard controller. The results demonstrate a computationally efficient, analytically guaranteed method for autonomous mid-air refueling that can handle realistic drogues and atmospheric disturbances, potentially informing future onboard autonomous refueling systems.

Abstract

In this paper, we present a control design methodology, stability criteria, and performance bounds for autonomous helicopter aerial refueling. Autonomous aerial refueling is particularly difficult due to the aerodynamic interaction between the wake of the tanker, the contact-sensitive nature of the maneuver, and the uncertainty in drogue motion. Since the probe tip is located significantly away from the helicopter's center-of-gravity, its position (and velocity) is strongly sensitive to the helicopter's attitude (and angular rates). In addition, the fact that the helicopter is operating at high speeds to match the velocity of the tanker forces it to maintain a particular orientation, making the docking maneuver especially challenging. In this paper, we propose a novel outer-loop position controller that incorporates the probe position and velocity into the feedback loop. The position and velocity of the probe tip depend both on the position (velocity) and on the attitude (angular rates) of the aircraft. We derive analytical guarantees for docking performance in terms of the uncertainty of the drogue motion and the angular acceleration of the helicopter, using the ultimate boundedness property of the closed-loop error dynamics. Simulations are performed on a high-fidelity UH60 helicopter model with a high-fidelity drogue motion under wind effects to validate the proposed approach for realistic refueling scenarios. These high-fidelity simulations reveal that the proposed control methodology yields an improvement of 36% in the 2-norm docking error compared to the existing standard controller.

Figures (8)

• Figure 1: Schematic of helicopter aerial refueling. $O$, $O_H$, $O_D$ correspond to the North-East-Down, helicopter's and drogue's coordinate frames, respectively. The velocity components, the rates of rotation, and the Euler angles in the $O_H$ coordinate frame are ($u,v,w$), ($p,q,r$) and ($\phi,\theta,\psi$), respectively. The velocity of the drogue is $u_D,v_D,w_D$. ($X,Y,Z$), ($X_P,Y_P,Z_P$), and ($X_D,Y_D,Z_D$) are the positions of the helicopter, the probe and the drogue, respectively. $\bar{x}$ is the vector that defines the probe tip in $O_H$.
• Figure 2: Generic inner-loop outer-loop control architecture.
• Figure 3: Proposed control architecture for helicopter aerial refueling.
• Figure 4: Schematic of the invariant set $\Lambda$ for the docking error. Different error trajectories initialized inside the invariant set denoted as $i_{1,2}$ remain in the invariant set. Error trajectory $i_{3}$ initialized outside the invariant set, converges to this invariant set. Increasing the uncertainty terms $\delta_D$, $\delta_R$ expands $\Lambda$ whereas the decreasing results in a smaller set $\Lambda$.
• Figure 5: Comparison between the proposed controller and the standard controller.

Theorems & Definitions (2)

• proof
• Corollary 1