Yongrong Sun

Modeling Method and Control Strategy for Hose-Drogue Aerial Refueling System

Conventional method for hose-drogue model of aerial refueling system is known to be complex due to the flexible body of hose. And as reported, drogues are unstable in atmospheric turbulence, which greatly decreases docking success rates. This paper proposes a dynamic model for a hose-drogue aerial refueling system based on Kane equation and rigid multi-body dynamics, and analyzes its performance. Furthermore, the nonlinear dynamic model is linearized at the equilibrium point and simplified from full order to 2nd order. Based on the simplified 2nd order model, active control strategies, including proportion integral derivative(PID) and liner quadratic regulator(LQR) control laws, are designed to inhibit the pendulum movement of drogue due to, atmospheric turbulences. Numerical simulation results show the significant correctness of the proposed dynamic model by steady-state drag and balance position of drogue when the tanker flights under different conditions. Moreover, the steady state position error varies within 1 cm, thanks to either controller, when the drogue suffers from moderate-level atmospheric turbulences. Further, the PID controller exhibits better control effect and higher control precision than LQR controller.

Real-time drogue detection and template tracking strategy for autonomous aerial refueling

Autonomous aerial refueling technology is an effective solution to extend flight duration of unmanned aerial vehicles, and also a great challenge due to its high risk. A novel real-time drogue detection and tracking strategy of monocular vision system for autonomous aerial refueling is proposed. It uses a direct image registration-based tracking method to ensure reliable and real-time tracking, and an ROI detection based on edge features to solve the tracking drift problem. A multiply patches fusion structure is adopted in the tracking method to improve the tracking accuracy and slow the divergence speed. Finally, various experiments are conducted to validate the proposed image processing strategy. These results show that the proposed strategy obtains a high accuracy as well as the real-time performance, and achieves a better performance than state-of-the-art methods.