International Journal of Aerospace Engineering

As the interest in probing deep space increases, it is necessary to enhance the autonomous navigation capabilities of the spacecraft. Since traditional navigation methods rely on ground-based radiometric tracking, the vehicle has a significant communication delay resulting in no ability to handle unexpected situations on time. Image-based optical navigation allows interplanetary spacecraft to determine their orbits autonomously. This paper explores how to accurately extract optical observations from the original images to perform autonomous navigation. First, we introduce a simple and efficient idea to locate valuable contours of the celestial body based on gradient variations. Then, we establish a rough estimation with RANSAC to remove the outliers around the edges. Next, we propose a refined estimation based on the hybrid genetic algorithm to precisely estimate the navigation observations. Lastly, numerous experiments have confirmed that our method achieves outstanding accuracy and robustness.

The landing phase of an airdrop process is prone to accidents, and thus, it is important to assess the landing reliability for an airdrop system. However, full field tests to assess the reliability are unacceptable due to their cost and the time required. As such, it is necessary to estimate the reliability in the design stage. To address this problem, a method based on vine-Bayesian Network (vine-BN) is proposed to assess the landing reliability by fusing multisource information. First, the network structure is determined by the relationship between data of simulation or ground tests and failure modes. Then, nodes are defined as random variables on [0, 1] based on the definition of the performance metric. Finally, the dependence between nodes is quantified by expert opinions. To illustrate the effectiveness of the method, a particular ground test or simulation is chosen to establish a network for a typical heavy cargo airdrop system (HCADS). Forward and backward propagation is carried out on the network. The forward analysis predicts the landing reliability in the design stage through multisource information fusion. Beta distribution is applied to fit the fusion result, so Bayesian inference is made to perform field test times decision-making. The backward analysis works to identify the key performance metrics related to landing reliability. The results and analysis manifest that vine-BN is feasible for fusing multisource information. Through the network, the reliability of the current design can be predicted effectively, and the field test times can be remarkably reduced. This method plays a crucial role in airdrop system design and reducing test time and labor.

The compressor geometric variable system is vital for aeroengines, as it affects their performance and design. To monitor the compressor geometric variable system states and detect anomalies in real time, a -step forecasting method based on the MAE (masked autoencoders) model was proposed in this article. Unlike previous studies that used simulated or lab-generated data, we use actual flight data recorded by the aircraft data acquisition system to make our results more realistic. Through our experimental efforts, the feasibility of forecasting the compressor geometric variable system based on the MAE model is verified. That is not only the first application of transformer models with a masked pretraining mechanism in time series forecasts but also taking the lead in exploring the possibility of this key system forecast. We also test the generalizability of our method across different types of aeroengines. Finally, to make our theories more reasonable and convincing, experiments on different aeroengine states, including the transition state and the steady state, are carried out.

To examine the impact of oxidative jets on the thrust vector angle, secondary combustion efficiency, and combustion chamber pressure, inert gas (nitrogen) and pure oxygen are injected into the primary flow, which includes combustible components, at various locations in the divergence section and throat using different injection techniques. The simulations utilize Reynolds-averaged Navier-Stokes equations coupled with the SST - turbulence model in two-dimensional numerical simulations and large-eddy simulation in three-dimensional studies. The numerical method is validated through schlieren experiments, and the vortex is identified using the Liutex-Omega method. The vortex structures and flow characteristics are analyzed. The results indicate that, at the same flow rate, the vector control effect of pure oxygen is superior to nitrogen only in the divergence section, but inferior to nitrogen in both the divergence section and throat. However, with improved vector control, the peak of the vector angle is achieved at a lower flow rate in the case of pure oxygen. When the secondary flow is introduced only in the divergence section, the flow ratio corresponding to the peak point in the pure oxygen case is approximately 14.3% earlier than that in the nitrogen case. The introduction of the pure oxygen jet enhances the secondary combustion efficiency of the primary flow, but to a limited extent. Additionally, when the jet is introduced at the throat, the effect of the pure oxygen case on adjusting the combustion chamber pressure is inferior to that of the nitrogen case. Concerning flow details, the trailing lower vortex replaces the trailing major vortex to become the highest magnitude vortex when the momentum flux ratio is small.

For space robots, it is difficult to track continuous time-varying manifolds on SE(3) by using traditional closed-loop control strategies, which are designed to track the position and the attitude separately. Therefore, the dynamics model should be rebuilt, and the corresponding control strategy should be redesigned. Firstly, the dynamics equations for a space robot in the joint space and workspace are established separately in the framework of Lie group SE(3) and screw theory based on the Lagrange principle. Secondly, based on the proposed feedback form, a PD (proportional derivative) control law of output force on the end-effector is designed, and a closed-loop continuous tracking control strategy is proposed using the force Jacobian matrix and the kinematic model. The simulation results show that the control scheme has good performance when the system state changes gently. Furthermore, a robust sliding mode tracking control scheme is designed. The simulation results show that the proposed robust control law has better accuracy than the PD control law because the system state changes wildly. Finally, a robust fuzzy sliding mode tracking control scheme is designed to deal with the chattering phenomenon. The simulation results show that the proposed robust fuzzy control law can eliminate the chattering well and decrease the joint control torque significantly. The robustness of the proposed robust fuzzy control law is also verified by numerical simulation.

Hypersonic vehicles are difficult to control due to their rapid time variation, dynamic nonlinearity, strong coupling, and model uncertainty. This paper proposes a new nonlinear predictive controller to solve the problem. An improved model predictive controller is used to improve the dynamic control performance of hypersonic vehicles by converting nonlinear dynamics into a state-dependent linear model. The sliding surface can significantly increase the speed of convergence. The radial basis function is used to reduce the influence of system uncertainty. The stability of the proposed controller is analyzed based on the Lyapunov approach. The comparison of simulation results verifies the excellent control performance of the proposed method both in convergence speed and antidisturbance ability.