Jun Li

Low-aspect-ratio flying-wing unmanned aerial vehicles (UAVs) are attractive drone platforms for civilian remote sensing, environmental monitoring, infrastructure inspection, disaster assessment, and persistent public-service monitoring because their integrated tailless layout offers high aerodynamic efficiency and payload volume. A trajectory-command-based three-loop nonlinear dynamic inversion (NDI) control architecture enhanced by a nonlinear disturbance observer (NDO) is designed to address the critical challenges of rapid time variation, strong nonlinearity, strong coupling, and restricted yaw authority in low-aspect-ratio flying-wing UAVs. The core innovation lies in the development of a trajectory-command-to-attitude kinematic mapping mechanism, integrated with the NDO for active torque compensation of lumped uncertainties and time-varying external disturbances. Leveraging a mathematical model of a low-aspect-ratio flying-wing UAV standard model, a three-loop NDI controller comprising angular rate, attitude, and trajectory command loops was designed based on the time-scale separation principle. The NDO was further designed to estimate lumped disturbances and provide feedforward compensation, thereby establishing an NDI-DO system that mitigates the high sensitivity of conventional NDI to modeling inaccuracies. Simulation and robustness tests involving typical high-angle-of-attack maneuvers (e.g., Cobra and Split-S maneuvers) demonstrated that the NDI-DO system achieved a reduction in angular-rate tracking error by over 77.2% compared to the baseline NDI. Furthermore, the permissible range of aerodynamic parameter perturbations was improved by 23%, significantly enhancing tracking fidelity and disturbance rejection. In a 3-DOF wind tunnel free-flight test, the NDI-DO system achieved a substantial expansion of the controllable angle-of-attack (attitude-stability) envelope from 72.9° to 99.19°, substantiating the high reliability and engineering utility of the control framework in post-stall nonlinear regimes. These results indicate that the proposed NDI-DO framework can support safer envelope expansion, autonomous upset recovery, and robust flight control for civilian flying-wing drones operating under uncertain aerodynamic and environmental conditions.

Accurate and computationally efficient trajectory tracking remains a critical challenge for unmanned aerial vehicles (UAVs), particularly when nonlinear model predictive control (NMPC) is combined with learning-based dynamics models that introduce significant computational burden. This paper proposes a sparse neural dynamics modeling approach by integrating structured pruning and robustness-enhancing fine-tuning techniques to enable efficient nonlinear MPC (NMPC) for UAV trajectory tracking. To this end, a structured neuron-level pruning strategy is introduced, combining L1-norm importance scores with adversarial sensitivity analysis to identify and remove redundant neurons from a neural dynamics model. To preserve smoothness and robustness in closed-loop control, spectral norm constraints and gradient regularization are further incorporated during fine-tuning. The resulting pruned neural dynamics model is embedded into an NMPC framework for online trajectory tracking. Simulation results on a fixed-wing UAV demonstrate that the proposed method reduces the number of trainable parameters by approximately 69% and achieves a 19% reduction in average NMPC solve time, leading to an effective control update frequency of about 39 Hz under the considered simulation settings. Compared with conventional controllers, including TECS and linear MPC, the proposed approach achieves significantly improved trajectory tracking accuracy, as reflected by lower MAE and RMSE across all position axes. These results indicate that structured sparsification of neural dynamics models provides an effective means to enhance both computational efficiency and tracking performance in NMPC-based UAV control.

Recently, the United States unveiled a conceptual design of an unmanned high-speed vehicle, the SR-72, which boasts a maximum flight speed of Mach 6, enabling rapid airspace dominance and superior combat performance. To this end, this study conducted a comprehensive review of publicly available data and employed 3D modeling software to reconstruct the SR-72 configuration, utilizing the supersonic thin airfoil NACA 16006 for the wing design. Subsequently, a meticulously structured computational mesh was generated. Numerical simulations were conducted across subsonic, transonic, supersonic, and high-Mach-number flow regimes. The results reveal that the vehicle exhibits high maneuverability in subsonic conditions, with a stall angle of attack reaching 24°. In transonic conditions, significant wave drag is observed, while, in supersonic and high-Mach-number flow regimes at Mach 6, the vehicle demonstrates excellent wave-riding performance, enabling extended cruise durations and improved fuel efficiency. Furthermore, the initial airfoil was optimized using the CST (Class-Shape Transformation) parameterization method and the SLSQP (Sequential Least Squares Programming) algorithm. Under the given constraints, the drag coefficient was reduced by 40%, demonstrating a significant optimization effect.

In this study, we introduce a novel method for an online trajectory replanning approach for fixed-wing Unmanned Aerial Vehicles (UAVs). Our method integrates moving obstacle predictions within a gradient-based optimization framework. The trajectory is represented by uniformly discretized waypoints, which serve as the optimization variables within the cost function. This cost function incorporates multiple objectives, including obstacle avoidance, kinematic and dynamic feasibility, similarity to the reference trajectory, and trajectory smoothness. To enhance prediction accuracy, we combine physics-based and pattern-based methods for predicting obstacle movements. These predicted movements are then integrated into the online trajectory replanning framework, significantly enhancing the system’s safety. Our approach provides a robust solution for navigating dynamic environments, ensuring both optimal and secure UAV operation.

The indirect method has long been favored for solving trajectory optimization problems due to its ability to reveal the structure of the corresponding optimal control. However, providing an appropriate initial guess of the co-state vector remains challenging due to its lack of physical significance. To address this issue, this paper introduces a Physics-Informed Indirect Method (PIIM), for which all shooting variables are constrained into a narrow space by utilizing their physics information. Starting from the Time-Optimal Soft Landing Problem (TOSLP), an analytical estimation method for the minimum flight time is provided; we show that the co-state vector at the final time can be constrained on a unit 3-D hypersphere by eliminating the mass co-state and the numerical factor used in the initial co-state vector normalization; and the physical significance of the optimal control at the final time is exploited, allowing one to further narrow down the solution space for the co-state vector to a unit 3-D octant sphere. Then, by incorporating the above physics-informed information into a shooting function that involves of propagating the dynamics of both states and co-states backward, the PIIM achieves faster and more robust convergence for the TOSLP compared to existing indirect methods. In addition, the combination of the PIIM with a homotopy approach is proposed, allowing one to solve the fuel-optimal soft landing problem robustly. Finally, numerical simulations are presented to demonstrate and validate the developments of the paper.

Aircraft secondary flow systems are small-flow circulation devices that are used for thermal and cold management, flow control, and energy generation on aircraft. The aerodynamic characteristics of main-flow-based inlets have been widely studied, but the secondary-flow-based small inlets, jets, and blowing and suction devices have seldom been studied. Two types of secondary flow systems embedded in a supersonic aircraft wing, a ram-air intake and a submerged intake, are researched here. Firstly, wind tunnel tests under subsonic, transonic, and supersonic conditions are carried out to test the total pressure recovery and total pressure distortion. Secondly, numerical simulations are used to analyze the flow characteristics in the secondary flow systems. The numerical results are validated with experimental data. The calculating errors of the total pressure recovery on the ram-air and submerged secondary flow systems are 8% and 10%, respectively. The simulation results demonstrate that the total pressure distortion tends to grow while the total pressure recovery drops with the increasing Mach number. As the Mach number increases from 0.4 to 2, the total pressure recovery of the ram-air secondary flow system decreases by 68% and 71% for the submerged system. Moreover, the total pressure distortion of the ram-air and submerged secondary flow systems is increased by 19.7 times and 8.3 times, respectively. Thirdly, a detailed flow mechanism is studied based on the simulation method. It is found that the flow separation at the front part of the tube is induced by adverse pressure gradients, which primarily determine the total pressure recovery at the outlet. The three-dimensional vortex in the tube is mainly caused by the change in cross-sectional shape, which influences the total pressure distortion.

In this paper we consider a trajectory planning problem arising from a lunar vertical landing with minimum fuel consumption. The vertical landing requirement is written as a final steering angle constraint, and a nonnegative regularization term is proposed to modify the cost functional. In this way, the final steering angle constraint will be inherently satisfied according to Pontryagin’s Minimum Principle. As a result, the modified optimal steering angle has to be determined by solving a transcendental equation. To this end, a transforming procedure is employed, which allows for finding the desired optimal steering angle by a simple bisection method. Consequently, the vertical landing trajectory can be found offline by the indirect shooting method. Finally, some numerical examples are presented to demonstrate and verify the developments of the paper.

This work is devoted to generating optimal guidance commands in real time for attitude-constrained solar sailcrafts in coplanar circular-to-circular interplanetary transfers. Firstly, a nonlinear optimal control problem is established, and necessary conditions for optimality are derived by the Pontryagin's Minimum Principle. Under some assumptions, the attitude constraints are rewritten as control constraints, which are replaced by a saturation function so that a parameterized system is formulated to generate an optimal trajectory via solving an initial value problem. This approach allows for the efficient generation of a dataset containing optimal samples, which are essential for training Neural Networks (NNs) to achieve real-time implementation. However, the optimal guidance command may suddenly change from one extreme to another, resulting in discontinuous jumps that generally impair the NN's approximation performance. To address this issue, we use two co-states that the optimal guidance command depends on, to detect discontinuous jumps. A procedure for preprocessing these jumps is then established, thereby ensuring that the preprocessed guidance command remains smooth. Meanwhile, the sign of one co-state is found to be sufficient to revert the preprocessed guidance command back into the original optimal guidance command. Furthermore, three NNs are built and trained offline, and they cooperate together to precisely generate the optimal guidance command in real time. Finally, numerical simulations are presented to demonstrate the developments of the paper.