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Outlier-resistant distributed fusion filtering for nonlinear discrete-time singular systems under a dynamic event-triggered scheme

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Frontiers of Information technology & Electronic engineering 2024年第2期25卷 237-249页

作者： Zhibin HU Jun HU Cai CHEN Hongjian LIU Xiaojian YI Department of Applied Mathematics Harbin University of Science and TechnologyHarbin150080China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and TechnologyHarbin150080China School of Automation Harbin University of Science and TechnologyHarbin150080China School of Mathematics and Physics Anhui Polytechnic UniversityWuhu241000China School of Mechatronical Engineering Beijing Institute of TechnologyBeijing100081China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing314003China Tangshan Research Institute Beijing Institute of TechnologyTangshan063099China

This paper investigates the problem of outlier-resistant distributed fusion filtering(DFF)for a class of multi-sensor nonlinear singular systems(MSNSSs)under a dynamic event-triggered scheme(DETS).To relieve the effect of measurement outliers in data transmission,a self-adaptive saturation function is ***,to further reduce the energy consumption of each sensor node and improve the efficiency of resource utilization,a DETS is adopted to regulate the frequency of data *** the addressed MSNSSs,our purpose is to construct the local outlier-resistant filter under the effects of the measurement outliers and the DETS;the local upper bound(UB)on the filtering error covariance(FEC)is derived by solving the difference equations and minimized by designing proper filter ***,according to the local filters and their UBs,a DFF algorithm is presented in terms of the inverse covariance intersection fusion *** such,the proposed DFF algorithm has the advantages of reducing the frequency of data transmission and the impact of measurement outliers,thereby improving the estimation ***,the uniform boundedness of the filtering error is discussed and a corresponding sufficient condition is ***,the validity of the developed algorithm is checked using a simulation example.

关键词： Distributed fusion filtering Multi-sensor nonlinear singular systems Dynamic event-triggered scheme Outlier-resistant filter Uniform boundedness

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Dynamic area coverage for Multi-USV with fully connected constraint based on game theory

Dynamic area coverage for Multi-USV with fully connected con...

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2022 Chinese Automation Congress, CAC 2022

作者： Han, Xiao Chen, Huan Gao, Yufeng Ma, Guangfu Wu, Shuanghong Hao, Wenxin Harbin Institute of Technology Department of Control Science and Engineering Harbin China China Ship Development and Design Center Wuhan China China Academy of Space Technology The Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (纸本)9781665465335

This paper proposes a novel method to solve the problem of regional surveys on the ocean in a complex environment. In order to solve this challenging regional survey problem considering fully connected, a new method combined artificial potential field and game theory is designed to maximize the coverage ratio. This paper gives the mathematical model of information transfer between two unmanned surface vehicles(USVs), and then the motion strategy of USVs is given based on the idea of game theory. The area coverage task of the multi-USV system is realized by a control method similar to the artificial potential field method. © 2022 IEEE.

关键词： Game theory

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A NTSMCLF Attitude Tracking controller for the Satellite with Flexible Accessories based on the Stewart platform

A NTSMCLF Attitude Tracking Controller for the Satellite wit...

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第42届中国控制会议

作者： Xixuan Chen He Zhu Junxiong Yin Yumeng Han Yanning Guo Department of Control Science and Engineering Harbin Institute of Technology Shanghai Academy of Spaceflight Technology

In this paper,a novel class of finite-time high precision satellites attitude tracking controller based on nonsingular terminal sliding mode control Lyapunov function(NTSMCLF) with the Stewart platform active vibration isolation is *** active vibration algorithm for Stewart platform is designed to isolate the micro-vibration,which uses sliding mode variable Structure control(SMC) *** order to suppress the influence of vibration on satellite,the dynamical model of Stewart platform considering the physical constraints,noise immunity and robustness of passive vibration isolation mechanical structures is *** the controller accuracy and infinite time convergence,the finite-time control Lyapunov function(CLF)high-precision attitude tracking control algorithm based on nonsingular terminal sliding mode(NTSM) is *** finitetime stability of the system is *** simulation results are provided to validate the effectiveness of proposed control method.

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Neural Network Based Active Fault-tolerant control of Spacecraft via Prescribed Performance

Neural Network Based Active Fault-tolerant Control of Spacec...

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Asian control Conference

作者： Ze Yang Baoqing Yang Ruihang Ji Jie Ma Control and Simulation Center Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper mainly studies the fault diagnosis and fault tolerance control of spacecraft attitude control system (ACS). Firstly, a novel model-free fault diagnosis network (FDN) is constructed based on Bi-LSTM neural networks. It uses only historical data to approximate the ACS mechanism, generate residuals, and combine adaptive thresholds to detect actuator and sensor faults. Secondly, to solve the problem of missing angular rate measurement caused by sensor faults, a fault-tolerant controller based on adaptive tracking differentiator (ATD) to generate virtual angular rate and have prescribed performance is designed to improve the robustness to faults and avoid performance degradation. Finally, simulation results are given to evaluate the effectiveness of the proposed scheme.

关键词： Fault diagnosis Space vehicles Fault tolerance Analytical models Actuators Attitude control Simulation

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EFEAR-4D: Ego-Velocity Filtering for Efficient and Accurate 4D radar Odometry

arXiv

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arXiv 2024年

作者： Wu, Xiaoyi Chen, Yushuai Li, Zhan Hong, Ziyang Hu, Liang The Department of Automation School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin150001 China

Odometry is a crucial component for successfully implementing autonomous navigation, relying on sensors such as cameras, LiDARs and IMUs. However, these sensors may encounter challenges in extreme weather conditions, such as snowfall and fog. The emergence of FMCW radar technology offers the potential for robust perception in adverse conditions. As the latest generation of FWCW radars, the 4D mmWave radar provides point cloud with range, azimuth, elevation, and Doppler velocity information, despite inherent sparsity and noises in the point cloud. In this paper, we propose EFEAR-4D, an accurate, highly efficient, and learning-free method for large-scale 4D radar odometry estimation. EFEAR-4D exploits Doppler velocity information delicately for robust ego-velocity estimation, resulting in a highly accurate prior guess. EFEAR-4D maintains robustness against point-cloud sparsity and noises across diverse environments through dynamic object removal and effective region-wise feature extraction. Extensive experiments on two publicly available 4D radar datasets demonstrate state-of-the-art reliability and localization accuracy of EFEAR-4D under various conditions. Furthermore, we have collected a dataset following the same route but varying installation heights of the 4D radar, emphasizing the significant impact of radar height on point cloud quality - a crucial consideration for real-world deployments. Our algorithm and dataset will be available soon at https://***/CLASS-Lab/EFEAR-4D. © 2024, CC BY-NC-SA.

关键词： Millimeter waves

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H-/H∞UIO-Based Robust Fault Diagnosis Design in Finite Frequency Domain for Discrete-Time Systems 41

H-/H∞UIO-Based Robust Fault Diagnosis Design in Finite Freq...

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41st Chinese control Conference, CCC 2022

作者： Gao, Sheng Wang, Jian Lu, Zhihui Ma, Guangfu Guo, Yanning Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Beijing Institute of Remote Sensing Equipment Beijing100005 China Aerospace System Engineering Shanghai Shanghai201109 China

ISBN: (纸本)9789887581536

This study investigates the problem of robust fault diagnosis for discrete-time systems subjected to actuator faults and unknown input disturbances. A novel H-/L-{infty} unknown input observer (UIO) based on the generalized Kalman-Yakubovich-Popov (GKYP) lemma is presented by decoupling the partial unknown input disturbances and attenuating the unknown input disturbances that cannot be decoupled. Then, the H- performance index in the finite frequency domain is used to measure the fault sensitivity and the L-{infty} performance index is used to reduce the influence of unknown input disturbances on fault diagnosis results and ensure the robustness performance. A residual evaluation function and a time-varying threshold are presented based on the L-{alpha} performance index. In addition, sufficient conditions for designing the H-/L-{infty} UIO are proposed and transformed into linear matrix inequalities (LMIs) that can be easily solved. Finally, simulations of a simple discrete-time system are used to validate the effectiveness of the developed H-/L-{infty} UIO. © 2022 Technical Committee on control theory, Chinese Association of Automation.

关键词： Linear matrix inequalities

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ACIGS: An automated large-scale crops image generation system based on large visual language multi-modal models

ACIGS: An automated large-scale crops image generation syste...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Bolong Liu Hao Zhang Jie Liu Qiang Wang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science and Technology Harbin Institute of Technology (ShenZhen) ShenZhen China

Smart agriculture requires an extensive convergence of information technology and agriculture. Attaining intelligence mandates an enormous amount of data to train models. However, it is challenging to acquire a large number of crop image data, limiting the application and growth of computer vision technology in agriculture. To address this problem, we designed a crop image generation system that combines a large language model with visual language multi-modal large models to augment the scale, variety, and resolution of crop image data. First, the system inputs existing real crop images into the visual language multimodal model to extract features and represent crop images in text form. Then, the system passes the crop text representation to the language model for cleaning and processing, which generates prompts to create crop images. The prompts are input into the visual language multi-modal model to generate crop images based on text representation of crops. The resulting crop images undergo image quality evaluation in the visual language multimodal model, and high-quality crop images are saved to the crop image dataset based on the quality evaluation. These steps lead to the formation of the final generated crop image dataset. The experimental results indicate that the crop images generated using the proposed system are similar to but different from the example images. This characteristic enables the expansion of crop data while circumventing redundancy and allowing for resolution control, which is crucial for dense segmentation tasks. Using this method, the existing data can be enlarged up to 7.5 times.

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Weight Design for Small Data Sample Decision under Attribute Association

Weight Design for Small Data Sample Decision under Attribute...

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2022 IEEE Asia-Pacific Conference on Computer Science and Data engineering, CSDE 2022

作者： Zhang, Shixuan Sun, Songpeng Ma, Jie Zhang, Kai Zheng, Xiaopeng Harbin Institute of Technology Department of Control Science and Engineering Harbin China Science and Technology on Space Physics Laboratory Beijing China China Academy of Launch Vehicle Technology Research and Development Department Beijing China

ISBN: (纸本)9781665453059

In aircraft test, the sample size of flight test data is small, and the test data returned by telemetry also has the problem of attribute association. A weight design method of small sample decision under attribute association is proposed. The method enhances the small sample data based on the synthetic minority oversampling technique, reduces the dimension of variables through principal component analysis, normalizes the principal components based on the variance contribution rate, and obtains the weight of each principal component after dimensionality reduction. The research results can be applied to the weight design of the guidance and control system of aircraft, and provide a new method support for the subsequent relevant test evaluation. © 2022 IEEE.

关键词： Principal component analysis

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A Fast Cloud Detection based on Improved 2D OTSU with Zynq SoC 15

A Fast Cloud Detection based on Improved 2D OTSU with Zynq S...

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15th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2021

作者： Yu, Ximing Liu, Zihao Peng, Yu Harbin Institute of Technology Department of Test and Control Engineering Harbin China Harbin Nosean Test and Control Technology Co. Ltd. Harbin China

ISBN: (纸本)9781665444910

Cloud detection plays an important role in pre-processing of the remote sensing image to locate the potential area of the target fast and improve the detection efficiency. Although the cloud detection performance is not ideal in the complex ground, such as bright buildings on the ground, the OTSU based method is used to achieve fast cloud detection onboard for the single scenarios due to the simple calculation process. However, due to the sequential processing capabilities provided by the onboard processors, the processing complexity of OTSU increases significantly as the image size increasing. In this article, a fast cloud detection method based on the improved 2D OTSU with Zynq system on chip (SoC) is proposed. The improved 2D OTSU method is utilized for cloud detection to provide the capability to distinguish highlight buildings on the ground. And the parallel processing for key process in the 2D OTSU provided by the field programmable gate array in Zynq SoC to improve computing efficiency. Compared with the 1D OTSU, experimental results show that the proposed method could achieve high detection precision. Meanwhile, the processing time is about 0.18s for a 512×512 size image, which is a relative fast speed for the practical application. © 2021 IEEE.

关键词： System-on-chip

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Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning

arXiv

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arXiv 2024年

作者： Qi, Biqing Gao, Junqi Chen, Xingquan Li, Dong Liu, Jianxing Wu, Ligang Zhou, Bowen Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Electronic Engineering Tsinghua University Beijing China School of Mathematics Harbin Institute of Technology Harbin China Frontis.AI Beijing China

Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent years for its pivotal role in addressing continuously arriving classes. However, it encounters additional challenges. The scarcity of samples in new sessions intensifies overfitting, causing incompatibility between the output features of new and old classes, thereby escalating catastrophic forgetting. A prevalent strategy involves mitigating catastrophic forgetting through the Explicit Memory (EM), which comprise of class prototypes. However, current EM-based methods retrieves memory globally by performing Vector-to-Vector (V2V) interaction between features corresponding to the input and prototypes stored in EM, neglecting the geometric structure of local features. This hinders the accurate modeling of their positional relationships. To incorporate information of local geometric structure, we extend the V2V interaction to Graph-to-Graph (G2G) interaction. For enhancing local structures for better G2G alignment and the prevention of local feature collapse, we propose the Local Graph Preservation (LGP) mechanism. Additionally, to address sample scarcity in classes from new sessions, the Contrast-Augmented G2G (CAG2G) is introduced to promote the aggregation of same class features thus helps few-shot learning. Extensive comparisons on CIFAR100, CUB200, and the challenging ImageNet-R dataset demonstrate the superiority of our method over existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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