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.

Birds and experienced glider pilots frequently use atmospheric updrafts for long-distance flight and energy conservation, with harvested energy from updrafts serving as the foundation. Inspired by their common characteristics in autonomous soaring, a reinforcement learning algorithm, the Twin Delayed Deep Deterministic policy gradient, is used to investigate the optimal strategy for an unpowered glider to harvest energy from thermal updrafts. A round updraft model is utilized to characterize updrafts with varied strengths. A high-fidelity six-degree-of-glider model is used in the dynamic modeling of a glider. The results for various flight initial positions and updraft strengths demonstrate the effectiveness of the strategy learned via reinforcement learning. To enhance the updraft perception ability and expand the applicability of the trained glider agent, an extra wind velocity differential correction module is introduced to the algorithm, and a strategy symmetry method is applied. Comparison experiments regarding round updraft, the Gedeon thermal model, and Dryden continuous turbulence indicate the crucial role of the further optimized methods in improving the updraft-sensing ability of the reinforcement learning glider agent. With optimized methods, a glider trained in a simplified thermal updraft with a simple training method can have more effective flight strategies.

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 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.