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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. [ABSTRACT FROM AUTHOR]

This paper proposes a novel back-stepping based flight controller for the receiver in Autonomous Aerial Refueling (AAR) by the combination of active disturbance rejection control (ADRC) and preview control methodology. Firstly, the proposed flight control law design is divided into five loops by the back-stepping technique. The 6 DOF model for aircraft is written into several strict-feedback nonlinear forms through tactful transformation, considering that the path dynamics are originally non-affine nonlinear forms and are intractable for controller design. Secondly, the influences of the unknown flow perturbations on a receiver in each loop are viewed as the components of the “total disturbances” which are estimated and compensated by extended state observer (ESO). Thirdly, the preview control methodology is introduced into the position loop design as the system dynamic of the receiver is much slower than drogue. The position loop determines the proper reference path angle by using a fuzzy-logic ADRC controller in which previewing error and current tracking error are both taken into consideration. And a novel adaptive look-ahead distance scheme based on the fuzzy logic control (FLC) is proposed for the selection of the preview point. Finally, extensive simulations and comparisons on the 6 DOF receiver model are carried out to demonstrate the effectiveness of the proposed flight controller. [ABSTRACT FROM AUTHOR]

Abstract In the autonomous aerial refueling (AAR) docking process, the bow wave generated by the receiver has a strong effect on the drogue, which affects the docking success rate greatly. Thus, a deep learning based trajectory optimization method which aims to decrease the bow wave effect on the drogue is proposed in this paper. There are mainly three parts in the proposed trajectory optimization method. Firstly, a precise bow wave model based on deep learning is presented to estimate the bow wave effect on the drogue. Furthermore, due to the dynamic characteristic of the drogue, a simple and practical drogue motion prediction model under multiple disturbances is carried out to provide a precise prediction of the drogue position at the next time. Moreover, considering the strict attitude constraints requirements in the AAR docking process, a novel reference observer is designed to estimate the receiver attitude from the optimized trajectory under wind perturbations. Then, the proposed trajectory optimization method could not only diminish the bow wave effect on the drogue largely but also satisfy the attitude constraints of the receiver. Finally, the effectiveness of the proposed method is demonstrated by the simulations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid approach based on the Lyapunov Guidance Vector Field (LGVF) and the Improved Interfered Fluid Dynamical System (IIFDS), to solve the problems of target tracking and obstacle avoidance in three-dimensional cooperative path planning for multiple unmanned aerial vehicles (UAVs). First, LGVF method is improved for UAV cooperative target tracking in 3D environment by introducing vertical component, with two guidance layers containing steering control and speed control. Second, IIFDS method is presented for UAVs to avoid obstacles or threats in complicated environment, where the local minimum problem is well resolved. Moreover, some cooperative strategies are added into the IIFDS framework to satisfy the constraints of obstacle avoidance and cluster maintenance. Finally, the missions of tracking target and avoiding obstacles can be performed simultaneously, by replacing the original sink fluid of IIFDS with the vector field of LGVF. Besides, the reactive parameters of IIFDS can be adjusted by the rolling optimization strategy to enhance the path quality. The experimental results validate the effectiveness of the hybrid method. [ABSTRACT FROM AUTHOR]

According to the tactical requirements of unmanned aerial vehicle (UAV) for tracking target and avoiding obstacle in complex dynamic environment, a three-dimensional (3D) real-time path planning method is proposed by combing the improved Lyapunov Guidance Vector Field (LGVF), the Interfered Fluid Dynamical System (IFDS) and the strategy of varying receding-horizon optimization from Model Predictive Control (MPC). First, in order to track the moving target in 3D environment, the LGVF method is improved by introducing flight height into the traditional Lyapunov function, and the generated velocity can guide UAV converge gradually to the limit cycle in horizontal plane and the optimal height in vertical plane. Then, the IFDS method imitating the phenomenon of fluid flow is utilized to plan the collision-free path. To achieve the mission of tracking moving target and avoid static or dynamic obstacle at the same time, the guidance vector field by LGVF is taken as the original fluid of IFDS. As the fluid system still remains stable under the influence of obstacles, the disturbed streamline from the interfered fluid can be regarded as the planned path. Third, as the quality of route is mainly influenced by the repulsive and tangential parameters of IFDS, the real-time suboptimal route can be planned by the varying receding-horizon optimization according to the predicted motion. The experimental results prove that the proposed hybrid method is applicable to various dynamic environments. [ABSTRACT FROM AUTHOR]

Aiming at the trajectory optimization of the solar-powered UAVs (SUAVs) cooperative target tracking in urban environment, the distributed model predictive control (DMPC) method based on Adaptive Grasshopper Optimization Algorithm (AGOA) is proposed in this paper. Firstly, the cooperative target tracking problem in urban environment is modeled by formulating the SUAVs kinematic and target models, the urban environment constraints, solar power harvesting and consumption models for SUAV, and sight occlusions by constructions. Especially, the sight occlusions in urban environment for SUAV are taken into consideration for the first time in this paper. A judgment method of sight occlusions for SUAV is proposed to make the calculation of the energy index more precise. Second, based on the precise modeling, the DMPC method is adopted as the framework for trajectory optimization in real time. Third, AGOA, a novel intelligent algorithm to mimic the behaviors of grasshoppers, is proposed to be the DMPC solver. The proposed AGOA has a better searching ability than the traditional GOA and some other intelligent algorithms by introducing some improvement measures e.g. the natural selection strategy, the democratic decision-making mechanism, and the dynamic feedback mechanism based on the 1/5 Principle. Finally, the effectiveness of the proposed method is demonstrated by the simulations. [ABSTRACT FROM AUTHOR]