Probe Dynamics Direct Control for Aerial Recovery With Preassigned Docking Performance

Abstract

Aiming at the probe docking control problem in aerial recovery, this article proposes a neural-adaptive probe dynamics direct control algorithm with preassigned docking performance for the unmanned aerial vehicle (UAV) to be recovered, even under multiple ambient airflow disturbances and initial docking deviations. The affine nonlinear dynamics of the probe in the inertial axis are formulated, together with the UAV's 6-DOF dynamics, for the convenience of the direct nonlinear docking control design. To compensate UAV's unmeasurable nonlinear dynamics with better approximation property and lower computational burden, the minimal learning parameter (MLP) technique based echo state network (ESN) approximators are constructed by employing the system state approximation errors to adaptively update neural weights learning. Then, to guarantee the preassigned docking performance required by the recovery safety, a finite-time prescribed performance control algorithm incorporated backstepping control law is developed to achieve the desired docking. Both the docking trajectory and forward docking velocity are not only always constrained within the preassigned performance constraints but also finely controlled to achieve accurate rendezvous with the target drogue. The closed-loop stability is discussed with Lyapunov analysis. Numerical simulations are conducted to validate the performance of the docking control algorithm.