Performance-Guaranteed Neuroadaptive Docking Control for UAV Aerial Recovery Under Carrier Maneuvering Flight
Aiming at reducing the airspace area requirement for aerial recovery, this work proposes a neuroadaptive maneuver docking control (NAMDC) scheme with appointed-time prescribed performance for an unmanned aerial vehicle (UAV) to be recovered. First, a six-degree-of-freedom (6-DOF) UAV model is establ...
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| Veröffentlicht in: | IEEE transactions on aerospace and electronic systems 2023-12, Vol.59 (6), p.1-15 |
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| creator | Wang, Yanxiang Wang, Honglun Liu, Yiheng Wu, Tiancai Zhang, Menghua |
| description | Aiming at reducing the airspace area requirement for aerial recovery, this work proposes a neuroadaptive maneuver docking control (NAMDC) scheme with appointed-time prescribed performance for an unmanned aerial vehicle (UAV) to be recovered. First, a six-degree-of-freedom (6-DOF) UAV model is established in the carrier frame to reflect the influence of the carrier movement on the UAV state. Then, an estimator-based minimal learning parameter neural network (EMLPNN) is developed for each subsystem to accurately approximate and compensate for the lumped disturbances with lower computational overhead. To guarantee the docking trajectory with preassigned transient and steady-state performance, an appointed-time prescribed performance control (APPC) algorithm is proposed and integrated with backstepping control. Furthermore, auxiliary systems are constructed to address the problem of input saturation by adjusting command signals. The stability of the closed-loop system is proved using a Lyapunov function. Finally, the effectiveness of the proposed method in the presence of carrier maneuvering flight, multiwind disturbances, different initial errors and actuator saturation is verified through numerical simulations. |
| doi_str_mv | 10.1109/TAES.2023.3305336 |
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First, a six-degree-of-freedom (6-DOF) UAV model is established in the carrier frame to reflect the influence of the carrier movement on the UAV state. Then, an estimator-based minimal learning parameter neural network (EMLPNN) is developed for each subsystem to accurately approximate and compensate for the lumped disturbances with lower computational overhead. To guarantee the docking trajectory with preassigned transient and steady-state performance, an appointed-time prescribed performance control (APPC) algorithm is proposed and integrated with backstepping control. Furthermore, auxiliary systems are constructed to address the problem of input saturation by adjusting command signals. The stability of the closed-loop system is proved using a Lyapunov function. 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| subjects | 6-DOF Actuators Algorithms Autonomous aerial vehicles Carrier mobility Closed loops Convergence Disturbances Docking Feedback control Flight Liapunov functions Maneuvers Mathematical models Neural networks Recovery Steady-state Subsystems Trajectory Transient analysis Unmanned aerial vehicles |
| title | Performance-Guaranteed Neuroadaptive Docking Control for UAV Aerial Recovery Under Carrier Maneuvering Flight |
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