Active disturbance rejection controllers optimized via adaptive granularity learning distributed pigeon-inspired optimization for autonomous aerial refueling hose-drogue system

Abstract

The autonomous aerial refueling (AAR) hose-drogue system (HDS) suffers the multi-wind disturbances leading to the low stability of drogue position, which is adverse for the successful probe-and-drogue AAR docking. This paper addresses the drogue position stability control problem in the presence of multi-wind disturbances. The finite-segment multi-body method is adopted to model the hose-drogue assembly as a link-connected system. A controllable drogue is equipped at the end of the hose to stabilize the drogue's relative position in the presence of tanker trailing vortex, receiver bow wave, atmospheric turbulence, and gust. Thus, the drogue active disturbance rejection controllers (ADRC) are designed to enhance the anti-disturbance ability and position stability of HDS. Besides, an improved pigeon-inspired optimization (PIO), adaptive granularity learning distributed PIO (AGLDPIO), is proposed to optimize the drogue lateral and vertical position controllers for realizing the optimal control effects and reducing the difficulties of parameter tuning. The simulation results show that the proposed optimized ADRC position controllers can effectively maintain the drogue swinging in a smaller range, which demonstrates the effectiveness and superiority of the proposed controllers.