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Autonomous Recommendation of Fault Detection Algorithms for Spacecraft

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 273-275页

作者： Wenbo Li Baoling Ning the National Key Laboratory of Science and Technology on Space Intelligent Control Beijing Institute of Control EngineeringBeijing 100094China the School of Data Science and Technology Heilongjiang UniversityHarbin 150001China

Dear Editor, This letter deals with the problem of algorithm recommendation for online fault detection of spacecraft. By transforming the time series data into distributions and introducing a distribution-aware measure, a principal method is designed for quantifying the detectabilities of fault detection algorithms over special datasets.

关键词： fault introducing letter

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Autonomous navigation method of satellite constellation based on adaptive forgetting factors

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Chinese Journal of Aeronautics 2024年第7期37卷 317-332页

作者： Dong WANG Jing YANG Kai XIONG School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control EngineeringBeijing 100094China

To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors(UKF-AFF)is *** process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting *** adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more *** intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency *** that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed *** analysis of the proposed method is also *** effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison *** estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.

关键词： Constellation autonomous navigation Unscented Kalman filter Adaptive forgetting factor Model uncertainty Stability analysis

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Identification and decoding technology of EEG under complex visual stimuli Using clustering ensemble

Identification and decoding technology of EEG under complex ...

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2024 China Automation Congress, CAC 2024

作者： Xu, Song Liu, Zhenfei Wang, Lina National Laboratory of Aerospace Intelligent Control Technology Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9798350368604

Accurate target recognition in complex backgrounds is the primary key step for aircraft to achieve sensitive perception and control. Traditional computer vision based object recognition technology is difficult to fully meet practical application needs for complex and open flight environment scenarios such as camouflage and variants. Its recognition accuracy and other performance are still far lower than manual interpretation. This article innovatively proposes an analysis method for identifying and decoding EEG signals under complex visual stimuli based on clustering ensemble. Firstly, this paper design the method for collecting and preprocessing EEG under visual stimulation obtain effective EEG information that can be represented in relevant visual stimuli;Further research was conducted on the time-domain and time-frequency feature extraction techniques for EEG signals to obtain EEG feature information with effective discriminative power under different stimulation;Finally, a clustering model for multi domain and multi-scale EEG mixed features was constructed, and a clustering ensemble model construction method for multiple subjects was designed to achieve strong generalization EEG identification and decoding across subjects under visual stimuli, thereby realizing perceptual recognition of relevant visual stimuli. The experimental results in this paper indicate that the proposed approach can precisely identify the target of interest in the image, promote the quantitative analysis of human brain signal representation models under visual stimuli, and promote the development of new brain inspired target recognition technologies. © 2024 IEEE.

关键词： Time domain analysis

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Unified Design Method of Filters for Different Measurement Phases in Space Relative Navigation Tasks

Unified Design Method of Filters for Different Measurement P...

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2024 China Automation Congress, CAC 2024

作者： Li, Qing Lu, Shan Zhang, Shiyuan Zhou, Jingjing Xu, Chen Shanghai Aerospace Control Technology Institute Shanghai China Shanghai Aerospace Control Technology Institute Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai China

ISBN: (纸本)9798350368604

In spacecraft relative navigation, as the relative distance changes, the output varies with different measurement devices. For traditional filtering methods, different filters need to be switched when the measurement output changes, which reduces the computational efficiency. Considering this problem, a simplified converted measurement Kaiman filter (SCMKF) is designed to construct a unified filter. The proposed unified filter simplifies the calculation of the measurement noise covariance matrix, based on the analysis on the factors affecting measurement noise and the accuracy of measurement device. Compared to the traditional filter, the proposed unified filter decreases the computational complexity significantly on the premise of ensuring the calculation accuracy. © 2024 IEEE.

关键词： Wiener filtering

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Accuracy analysis for distributed dynamic state estimation in large-scale systems with a cyclic network graph

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science China(Information sciences) 2023年第9期66卷 78-93页

作者： Mingyan ZHU Rui WANG Xiaodong MIAO Tianju SUI School of Control Science and Engineering Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Aeronautics and Astronautics Dalian University of Technology Key Laboratory of Advanced Technology for Aerospace Vehicles School of Mechanical and Power Engineering Nanjing Tech University

This paper addresses a distributed dynamic state estimation problem in large-scale systems characterized by a cyclic network graph. The objective is to develop a distributed estimation algorithm for each node to generate local state estimations, based on the coupled measurements and boundary information exchanged with neighboring nodes. Our proposed approach is grounded in the maximum a posteriori(MAP)estimation method, which yields suboptimal results in acyclic network graphs compared with the centralized MAP approach. We extend this approach to systems with a cyclic network graph. Furthermore, we provide an accuracy analysis by deriving bounds for the differences in estimation error covariance and state estimation between the proposed distributed algorithm and the suboptimal centralized MAP method. These bounds apply to a specific category of systems that satisfy certain conditions, including cyclic topology and sparse connections. We demonstrate that these bounds converge asymptotically, with the rate of convergence determined by the loop-free depth of the graph. The loop-free depth of the graph refers to the maximum number of nodes that can be traversed in a cycle without revisiting any node. Finally, we demonstrate the validity of the algorithm through numerical examples.

关键词： distributed state estimation maximum a posteriori estimation cyclic network graph

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A Novel Principal Component Analysis Algorithm for Fault Detection 43

A Novel Principal Component Analysis Algorithm for Fault Det...

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43rd Chinese control Conference, CCC 2024

作者： Shenhang, Wang Kun, Tian Wei, Zheng Mengyu, Liu Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing100854 China

ISBN: (纸本)9789887581581

In modern industry, fault detection is very important in high-cost and safety-critical processes. Principal component analysis (PCA) is an effective data-driven approach which is widely used in this field. Conventional PCA is time-invariant, which often lead to false alarms. In order to overcome the false alarms, recursive PCA algorithms have been proposed for adaptive process. However, the amount of calculation of the algorithm is very great because of frequently updated information. In this paper, a novel recursive PCA method is proposed for solving the question by introducing the standard residual method. The standard residual method serves as an indicator of the level of consistency among data, which is used to decide whether or not to update the PCA model. The proposed method is applied to fault detection and experiment results demonstrate the effectiveness of the method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Errors

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An improved high-fidelity adaptive model for integrated inlet-engine-nozzle based on mechanismdata fusion

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Chinese Journal of Aeronautics 2024年第8期37卷 190-207页

作者： Chen WANG Ziyang YU Xian DU Ximing SUN School of Control Science and Engineering Dalian University of TechnologyDalian 116024China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of EducationDalian University of TechnologyDalian 116024China

Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion *** component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic *** paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental ***,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is *** study reveals the inlet model’s efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational *** model’s output closely aligns with typical experimental *** combining offline optimization and online adaptive correction,the mechanismdata fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight ***,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location *** approach significantly curbs performance deviations in supersonic *** example,at Ma=2.0,the system error impressively drops from 34.17%to merely 6.54%,while errors for other flight conditions consistently stay below the 2.95%*** findings underscore the clear superiority of the proposed method.

关键词： Aero-propulsion system Integrated inlet-enginenozzle Component-level model On-board adaptive model Mechanism-data fusion Extreme learning machine

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Aeroengine thrust estimation and embedded verification based on improved temporal convolutional network

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Chinese Journal of Aeronautics 2024年第1期37卷 106-117页

作者： Wanzhi MENG Zhuorui PAN Sixin WEN Pan QIN Ximing SUN Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of TechnologyDalian 116024China School of Control Science and Engineering Dalian University of TechnologyDalian 116024China

Thrust estimation is a significant part of aeroengine thrust control *** traditional estimation methods are either low in accuracy or large in *** further improve the estimation effect,a thrust estimator based on Multi-layer Residual Temporal Convolutional Network(M-RTCN)is *** solve the problem of dead Rectified Linear Unit(ReLU),the proposed method uses the Gaussian Error Linear Unit(GELU)activation function instead of ReLU in residual *** the overall architecture of the multi-layer convolutional network is adjusted by using residual connections,so that the network thrust estimation effect and memory consumption are further ***,the comparison with seven other methods shows that the proposed method has the advantages of higher estimation accuracy and faster convergence ***,six neural network models are deployed in the embedded controller of the micro-turbojet *** Hardware-in-the-Loop(HIL)testing results demonstrate the superiority of M-RTCN in terms of estimation accuracy,memory occupation and running ***,an ignition verification is conducted to confirm the expected thrust estimation and real-time performance.

关键词： Thrust estimation Temporal convolutional network Embedded deployment Hardware-in-the-loop testing Ignition verification

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Continuous Self-Leaming control under System Identification based on Hopfield Neural Network

Continuous Self-Leaming Control under System Identification ...

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2024 China Automation Congress, CAC 2024

作者： Tan, Tian-Le Zhu, He Hou, Yue-Yang Zhang, Wen-Jing Shanghai Aerospace Control Technology Institute Shanghai Key Laboratory of Space Intelligent Control Technology Shanghai China

ISBN: (纸本)9798350368604

Considering the dynamic modeling of an unknown and time-varying complex dynamic system in the model-based control, this paper develops a data-driven system identification method based on the Hopfield neural network (abbreviated as HNN). The normalization and denormalization transformation are designed to preprocess the training samples, and by the sliding window to refresh the sampling continuously, an online iteration method is proposed to identify the dynamic parameters. Furthermore, a continuous self-learning control based on HNN is developed in coordination with the model-based prediction and inversion control. Simulation results on the manipulator show that the HNN could efficiently identify the dynamic model in real time. The identified model has an approximately isomorphic equivalent form to the dynamic mechanism model, which could be used for the qualitative and quantitative analysis of the control quality. Results also verify the dynamic adaptability of the HNN continuous self-learning control for the nonlinear unknown and time-varying system. © 2024 IEEE.

关键词： Hopfield neural networks

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Optimal crater landmark selection based on optical navigation performance factors for planetary landing

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Chinese Journal of Aeronautics 2023年第3期36卷 254-270页

作者： Yi XIU Shengying ZHU Rui XU Maodeng LI School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration Ministry of Industry and Information TechnologyBeijing 100081China Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of EducationBeijing 100081China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control EngineeringBeijing 100091China

Planetary craters are natural navigation landmarks that widely exist and are easily *** navigation based on crater landmarks has become an important autonomous navigation method for planetary *** to the increase in observed crater landmarks and the limitation of onboard computation,the selection of good crater landmarks has gradually become a research hotspot in the field of landmark-based optical *** paper designs a fast crater landmark selection method,which not only considers the configuration observability of crater subsets but also focuses on the influence on navigation performance arising from the measurement uncertainty and the matching confidence of craters,which is different from other landmark selection *** factor of measurement uncertainty,which is anisotropic,correlated and nonidentically distributed,is quantified and integrated into selection based on crater pairing detection and localization error *** addition,the concept of the crater matching confidence factor is introduced,which reflects the possibility of 2D projection measurements corresponding to 3D *** with the configuration observability factor,the crater landmark selection indicator is ***,the effectiveness of the proposed method is verified by Monte Carlo simulations.

关键词： Crater-based optical navigation Crater landmark selection Crater matching Measurement uncertainty Planetary landing

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