The problem of UAV trajectory tracking is a difficult issue for scholars and engineers, especially when the target curve is a complex curve in the three-dimensional space. In this paper, the coordinate frames during the tracking process are transformed to improve the tracking result. Firstly, the basic concepts of the moving frame are given. Secondly the transfer principles of various moving frames are formulated and the Bishop frame is selected as a final choice for its flexibility. Thirdly, the detailed dynamic equations of the moving frame tracking method are formulated. In simulation, a moving frame of an elliptic cylinder helix is formulated precisely. Then, the devised tracking method on the basis of the dynamic equations is tested in a complete flight control system with 6 DOF nonlinear equations of the UAV. The simulation result shows a satisfactory trajectory tracking performance so that the effectiveness and efficiency of the devised tracking method is proved.

The precise docking of fixed-wing unmanned aerial vehicles (UAVs) onto shipboard recovery systems faces significant control challenges due to dynamic environmental disturbances and the relative motion between the SideArm and UAVs. This paper presents a constraint-based control framework for UAV docking under irregular ship motions, ensuring seamless trajectory-attitude coordination with the arresting cable and SideArm's rail. To achieve this, a flexible docking-aware prescribed performance control (FDAPPC) method is developed to maintain the UAV's position and attitude within predefined constraints throughout the docking process, thereby enhancing docking accuracy. Furthermore, to mitigate the issue of flight-envelope exceedance caused by motion compensation during UAV approach, an adaptive adjustment envelope mechanism is proposed. An unknown system dynamic estimator (USDE) is also established to estimate the lumped external perturbations, augmenting the controller with feedforward compensation to enhance the UAV's disturbance rejection capabilities. Finally, numerical simulations under varying sea state conditions verify that the proposed method improves docking accuracy and robustness.

In this paper a new error function designed on 3-dimensional special Euclidean group SE(3) is proposed for the guidance of a UAV (Unmanned Aerial Vehicle). In the beginning, a detailed 6-DOF (Degree of Freedom) aircraft model is formulated including 12 nonlinear differential equations. Secondly the definitions of the adjoint representations are presented to establish the relationships of the Lie groups SO(3) and SE(3) and their Lie algebras so(3) and se(3). After that the general situation of the differential equations with matrices belonging to SO(3) and SE(3) is presented. According to these equations the features of the error function on SO(3) are discussed. Then an error function on SE(3) is devised which creates a new way of error functions constructing. In the simulation a trajectory tracking example is given with a target trajectory being a curve of elliptic cylinder helix. The result shows that a better tracking performance is obtained with the new devised error function.

This paper presents a neural network (NN) based model for hysteresis nonlinearity with multivalued mapping. It is proved that the Preisach-type hysteresis can be transformed into a general continuous mapping such as one-to-one or multivalued-to-one mapping, which can be approximated by a universal approximator. The main advantage is that the proposed model is suitable to different working conditions by adjusting the weights of NNs. Finally, the derived model is verified by modelling the behavior of hysteresis involved in a piezoelectric actuator.

Global navigation satellite system (GNSS) standalone or GNSS/Inertial Navigation System integrated apparatus is the essential part of a low-altitude Unmanned aerial vehicle (UAV) to achieve reliable location information. However, GNSS is fragile and easily attacked, it is critical to develop other absolute location methods for UAVs under GNSS-denied environments. In this paper, an aerial-satellite (AS) image matching based visual location system (AS-VPS) is proposed. Two key modules ‘front-end visual odometry’ and ‘back-end absolute location’ are included in the AS-VPS. A deep learning method ‘SuperPoint + LightGlue’ is utilized for front-end consecutive aerial image matching for visual odometry and back-end AS image matching for absolute location. In view of the characteristic that the back-end absolute location is not continuous, a factor graph optimization (FGO) method is then employed to integrate the position results from the front-end and back-end modules, and final optimized results are generated. In addition, a cooperative AS-VPS system is set up using the FGO method with inter-agent ranging measurement. In the experiment, real-world flight-testing datasets are collected to assess the performance of the proposed AS-VPS method, and the experimental result demonstrates the superior performance of the proposed method without GNSS.