This paper focuses on the hypersonic vehicle in consideration of oscillation and aerodynamic heating problems during reentry process, for trajectory optimization, penalty function method and ε level comparison are introduced for transforming the unconstrained waterweeds algorithm into constrained algorithm to solve multi-objective optimization problems, in which a novel and more precise criterion based on time-scales separation guidance for oscillation optimization is put forward indicating waterweeds algorithm to get a better optimal result. The algorithm transformed is successfully applied to trajectory design.

Trajectory Optimization

Separation (statistics)

Penalty Method

Oscillation (cell signaling)

10.1109/cgncc.2016.7828785

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Citations (3)

Reentry Guidance for Hypersonic Vehicle based on Reinforcement Learning

Ruiheng Liu Langfu Cui Bolun Gao Yidi Yao Qingzhen Zhang

For the problem of reentry guidance with online no-fly zone constraints, this paper combines the improved A* algorithm with DDPG and proposes a trajectory planning algorithm with high real-time property. Firstly, this paper constructs 2D environment grid map with online no-fly zone information. Secondly, this paper proposes a waypoint decision algorithm by conducting improved A* algorithm on the above map. The reentry guidance of the aircraft is thus simplified to the trajectory planning subtasks between waypoints. Finally, this paper proposes an aircraft trajectory generation algorithm. The algorithm first constructs the MDP model based on the requirements of the subtasks; the builds the DDPG agent training process. Through offline training simulation, the optimal parameters of the agent net is finally obtained. Finally, the aircraft uses the above agent to output the online bank angle guidance law and the expected trajectory of the aircraft.

Waypoint

Reentry

Trajectory Optimization

10.1109/icfeict59519.2023.00076

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Synthetic aperture radar raw signal simulation based on inverse chirp scaling algorithm

International Journal of Service and Computing Oriented Manufacturing (2016)

Xiaojuan Cheng Qingzhen Zhang Fei Tao

By the analysis of the imaging principle and the raw echo model of synthetic aperture radar (SAR) (Bamler et al., 1992), an efficient simulation method is proposed based on inverse imaging algorithm for SAR raw echo data (Franceschetti et al., 1995). The idea of the inverse imaging algorithm is based on chirp scaling (CS) imaging algorithm called inverse chirp algorithm (ICS) (Xiao and Pi, 2003). It converts the SAR scene greyscale into the confocal and complex image data comprising the continuous phase information, using a scene processing method (Li et al., 1996). Then, a backward method is used. It can inverse and simulate SAR scene raw echo data after azimuth decompression, range decompression and range curvature compression (Khwaja, 2006). Finally, the imaging simulation test is carried out to verify the effectiveness of this method.

Chirp

Image formation

10.1504/ijscom.2016.075417

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A novel search algorithm based on waterweeds reproduction principle for job shop scheduling problem

The International Journal of Advanced Manufacturing Technology (2015)

Lin Cheng Qingzhen Zhang Fei Tao Kun Ni Yang Cheng

Benchmark (surveying)

Job shop

Swarm intelligence

10.1007/s00170-015-8023-0

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Citations (15)

Remaining Useful Life Prediction for Aero-Engines Based on Time-Series Decomposition Modeling and Similarity Comparisons

Aerospace (2022)

Mingxian Wang Hongyan Wang Langfu Cui Gang Xiang Xiaoxuan Han

The aero-engine is the heart of an aircraft; its performance deteriorates rapidly due to the high temperature and high-pressure environment during flights. It is necessary to predict the remaining useful life (RUL) to improve the reliability of aero-engines and provide security for reliable flights. In previous flights, the sensors collected a lot of performance parameter data and formed a database regarding the aero-engine degradation process. These performance parameters cannot reflect the degradation process directly. In this paper, fuzzy clustering is applied to divide the degradation stages of the aero-engine, construct the health indicator, and describe the degradation process. Time-series decomposition modeling is applied to predict the degradation process of the health indicator. Based on the idea of similarity comparison, the RUL is predicted by comparing the similarity of time series through example learning. The method is verified and analyzed on the dataset published by National Aeronautics and Space Administration (NASA), and the mean square error (MSE) is 528. The result is better than the comparative method.

Similarity (geometry)

Degradation

Aero engine

10.3390/aerospace9100609

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Citations (7)

Data.rar

Figshare (2017)

Kun Ni Shaojun Han Lin Cheng Qingzhen Zhang Qi Wu

These data files are related to the research article "Robust Virtual Target Guidance for the Fixed-trim Vehicle under Multi-constraints".

10.6084/m9.figshare.4524461

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A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

Remote Sensing (2021)

Langfu Cui Qingzhen Zhang Liman Yang Chenggang Bai

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

Sliding window protocol

10.3390/rs13234864

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Citations (3)

Remaining useful life distribution prediction framework for lithium-ion battery fused prior knowledge and monitoring data

Measurement Science and Technology (2023)

Mingxian Wang Gang Xiang Langfu Cui Qingzhen Zhang Juan Chen

Abstract Remaining useful life (RUL) prediction is the main approach to guarantee the reliability of lithium-ion batteries. This paper proposes an interpretable hybrid method to predict the RUL distribution with changeable form. The method integrates prior knowledge from the statistical model and regular patterns learned from monitoring data based on the data-driven model. The predicted compound distribution provides more information compared to point estimation and distribution with fixed form. The general hybrid framework contains a component learner, a fusion model with a stacking strategy, and a prognostic distribution algorithm with adaptive sampling weights. The stacking fusion model is implemented by a one-dimensional convolution neural network. The sampling weights are estimated by optimal estimation. The statistical model describes the individual capacity degradation path based on the Wiener process. The data-driven model learns the degradation process from historical data based on convolutional gated recurrent neural network (CNN-GRU) and Monte Carlo dropout simulation. The comparative experiments between the proposed method and existing methods were carried out. The experiment results show that the proposed hybrid method performs well.

Dropout (neural networks)

Convolution (computer science)

10.1088/1361-6501/ace925

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Citations (7)