The construction of a six-degree-of-freedom (6-DOF) model for the composite motion of the actual mechanical structure (defined as an all-true composite motion model) of unmanned aerial vehicles (UAVs) is a prerequisite for achieving stable control of rotorcraft UAVs. Therefore, this paper proposes a construction approach for a nonlinear 6-DOF model of quadrotor and dual-rotor coaxial UAVs based on all-true composite motion. Two types of attitude–altitude control systems for rotorcraft based on a self-optimizing intelligent proportional–integral–derivative (PID) control method are constructed. Three-dimensional geometric models of the two rotorcraft types, incorporating their physical characteristics, are built. The attitude responses to different pulse width modulation (PWM) inputs are tested, thereby verifying the accuracy of the all-true composite model and analyzing the stability of the two types of UAVs. Furthermore, two types of attitude–altitude control inner loop controllers are designed, and the intelligent PID control algorithm is used to optimize the control parameters. Further verification of the robustness of the optimized parameters is carried out, and the designed attitude controllers are verified via experiment using a turntable. The simulation and experimental results show that the proposed all-true composite motion model and controller design method can accurately simulate the dynamic characteristics of the two types of UAVs and maintain stable attitude control, thus providing a valuable reference for the accurate attitude control of rotorcraft UAVs based on all-true composite motion.

Unmanned aerial vehicle (UAV)-based object detection methods are widely used in traffic detection due to their high flexibility and extensive coverage. In recent years, with the increasing complexity of the urban road environment, UAV object detection algorithms based on deep learning have gradually become a research hotspot. However, how to further improve algorithmic efficiency in response to the numerous and rapidly changing road elements, and thus achieve high-speed and accurate road object detection, remains a challenging issue. Given this context, this paper proposes the high-efficiency multi-object detection algorithm for UAVs (HeMoDU). HeMoDU reconstructs a state-of-the-art, deep-learning-based object detection model and optimizes several aspects to improve computational efficiency and detection accuracy. To validate the performance of HeMoDU in urban road environments, this paper uses the public urban road datasets VisDrone2019 and UA-DETRAC for evaluation. The experimental results show that the HeMoDU model effectively improves the speed and accuracy of UAV object detection.

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.

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.

The rapid, efficient, and rational design of airfoils is crucial for enhancing the flight efficiency of aircraft. At present, traditional two-dimensional airfoil design methods are mostly based on the indirect transformation of the features of existing airfoil libraries, which makes it challenging to develop new airfoil characteristics to meet the increasingly stringent flight index requirements. Therefore, a direct airfoil design path with less time consumption and high efficiency and a new method integrating direct airfoil generation with predicting aerodynamic performance parameters were put forward. First, an in-depth theoretical analysis of the turbulent characteristics of airfoils was conducted, which can more accurately predict performance parameters, such as the lift-drag ratio, lift coefficient, and drag coefficient of airfoils. Second, an airfoil generation strategy based on the “direct ellipse method” was proposed. Taking the ellipse as the geometric carrier, the upper curve of the airfoil is rapidly generated by combining the random three-point descent curve and the parameter calculation theory, and the overall design of the airfoil is efficiently accomplished through coordinate transformation. Finally, based on the airfoil coordinate points and a large amount of lift–drag ratio data calculated by Python programs and computational fluid dynamics programs, an airfoil lift–drag ratio prediction model based on deep learning multilayer perceptron theory was constructed. Compared with the traditional finite element calculation method, this model significantly accelerates the calculation speed of the lift–drag ratio, and the error rate is only 2.783%. Combining the new airfoil generation method and the lift–drag ratio prediction model can complete the overall airfoil design in a diverse, rapid, and accurate way, providing a reliable approach and valuable reference for efficient and rapid airfoil design.