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第35届中国控制会议

作者： Xing Xin Zhen LI Xiangyuan Zeng Xiangdong Liu Zhi Yang Song Xie School of Automation Beijing Institute of Technology Chinese National Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology DFH Satellite co. Ltd

ISBN: (纸本)9781509009107

An integrated simulation system is proposed in the paper,which considers the universal runtime platform environment,the resource management mechanism,and the aerospace oriented model software *** platform is compatible with the HLA protocol and support both synchronous and asynchronous modes for data *** simulation execution is established on the specifically formulated scenarios,which provides flexibilities in customizing the organization of the simulation *** resource framework is designed by combining the characteristics of both aerospace engineering and software engineering,and the modeling objects are expanded from the entities of satellites,sub-systems and equipments,to the non-entity relationships of the system *** the developed model database and management software,the simulation can be applied either in the visible and non-programming way,or in a secondary development way with ***,the proposed system is user-friendly to engineers with various disciplinary backgrounds.

关键词： Universal Simulation simulation platform framework small Satellite model Software

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Data Augment Using Deep Convolutional Generative Adversarial Networks for Transient Stability Assessment of Power systems

Data Augment Using Deep Convolutional Generative Adversarial...

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第三十九届中国控制会议

作者： Jiamin Li Hongying Yang Liping Yan Zonghan Li Daowei Liu Yuanqing Xia Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology China Electric Power Research Institute

Real–time and accurate transient stability assessment(TSA) is essential for planning, operation and control of power systems. As a data–driven technology, deep learning method plays an important role in TSA. Nevertheless, the fact that instability situations rarely occur would lead to a challenging class–imbalanced issue, which brings great difficulties to the deep learning methods. Besides, feature extraction from high dimensional input data and transient stability classification seem extremely difficult for conventional classification methods. To address these problems, this paper develops a class–imbalanced TSA method by combining nonlinear data synthesis method with the deep learning classification model. Firstly, deep convolutional generative adversarial network(DCGAN) is conducted to generate unstable instances based on the existing samples to balance the proportion of different classes. Furthermore, the convolutional neural network(CNN) is utilized to extract the nonlinear mapping relationship between the disturbance features and the stability category and realize TSA. Finally, the IEEE 10–machine, 39–bus New England system is utilized to verify the validity and effectiveness of the proposed method.

关键词： Class–imbalanced Data Generation DCGAN CNN TSA Power systems

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Social Energy:Mining Energy From the Society

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 466-482页

作者： Jun Jason Zhang David Wenzhong Gao Yingchen Zhang Xiao Wang Xiangyang Zhao Dongliang Duan Xiaoxiao Dai Jun Hao Fei-Yue Wang Department of Electrical and Computer Engineering Ritchie School of Engineering and Computer ScienceUniversity of Denver IEEE National Renewable Energy Laboratory Qingdao Academy of Intelligent Industries(QAII) QingdaoShandongChinaand SKL-MCCSCASIA School of Automation Science and Electrical Engineering Beihang University Department of Electrical and Computer Engineering College of Engineering and Applied ScienceUniversity of Wyoming State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Research Center of Computational Experiments and Parallel Systems National University of Defense Technology

The inherent nature of energy,i.e.,physicality,sociality and informatization,implies the inevitable and intensive interaction between energy systems and social *** this perspective,we define "social energy" as a complex sociotechnical system of energy systems,social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system *** recent advancement in intelligent technology,including artificial intelligence and machine learning technologies,sensing and communication in Internet of Things technologies,and massive high performance computing and extreme-scale data analytics technologies,enables the possibility of substantial advancement in socio-technical system optimization,scheduling,control and *** this paper,we provide a discussion on the nature of energy,and then propose the concept and intention of social energy systems for electrical power.A general methodology of establishing and investigating social energy is proposed,which is based on the ACP approach,i.e., "artificial systems"(A), "computational experiments"(C) and "parallel execution"(P),and parallel system methodology.A case study on the University of Denver(DU) campus grid is provided and studied to demonstrate the social energy *** the concluding remarks,we discuss the technical pathway,in both social and nature sciences,to social energy,and our vision on its future.

关键词： ACP distribution locational marginal pricing intelligent operation in power systems parallel control parallel intelligence parallel management social energy socio-technical energy systems

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High-Flexibility Locomotion and Whole-Torso control for a Wheel-Legged Robot on Challenging Terrain*

High-Flexibility Locomotion and Whole-Torso Control for a Wh...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Kang Xu Shoukun Wang Xiuwen Wang Junzheng Wang Zhihua Chen Daohe Liu Key Laboratory of Drive and Control of Servo Motion System Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

In this paper, we propose a parallel six-wheel-legged robot that can traverse irregular terrain while carrying objectives to do heavy-duty work. This robot is equipped with six Stewart platforms as legs and tightly integrates the additional degrees of freedom introduced by the wheels. The presented control strategy with physical system used to adapt the diverse degrees of each leg to irregular terrain such that robot increases the traversability, and simultaneously to maintain the horizontal whole-torso pose. This strategy makes use of Contact Scheduler (CS) and Whole-Torso control (WTC) to control the multiple degrees of freedom (DOF) leg for performing high-flexibility locomotion and adapting the rough terrain like actively parallel suspension system. We conducted experiments on flat, slope, soft and sand-gravel surface, which validate the proposed control method and physical system. Especially, we attempt to traverse over sand-gravel terrain with 3 people about 240kg payload.

关键词： Legged locomotion Wheels Torso Force Kinematics

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Data-Driven Model for Detection, Isolation and Accommodation of Faulty Angle of Attack Sensor Measurements in Fixed Winged Aircraft

Data-Driven Model for Detection, Isolation and Accommodation...

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Chinese control and decision Conference, CCDC

作者： Bemnet Wondimagegnehu Mersha Hongbin Ma School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665478977

Ethiopian Airlines’ Boeing 737-8 MAX nosedived and crashed shortly after takeoff on March 10, 2019, at Ejere Town, south of Addis Ababa. A faulty angle of attack (AOA) sensor was the cause of the crash. Many airplane accidents have been linked to faulty AOA sensors in the past. The majority of the AOA sensor fault detection, isolation, and accommodation (SFDIA) literature relied on linear model-driven techniques, which are not suitable when the system’s model is uncertain, complex, or nonlinear. Traditional multilayer perceptron (MLP) models have been employed in data-driven models in the literature and the effectiveness of deep learning-based data-driven models has not been investigated. In this work, a data collection and processing method that ensures the collected data is not monotonous and a data-driven model for AOA SFDIA is proposed. The proposed model uses a deep learning-based recurrent neural network (RNN) to accommodate for faulty AOA measurement under flight conditions with faulty AOA measurement, faulty total velocity measurement, and faulty pitch rate measurement. Conventional residual analysis with a fixed threshold is used to detect and isolate faulty AOA sensors. The proposed and benchmark models are trained with the adaptive momentum estimation (Adam) algorithm. We show that the proposed model effectively detects, isolates, and accommodates faulty AOA measurements when compared to other data-driven benchmark models. The method is able to detect and isolate faulty AOA sensors with a detection delay of 0.5 seconds for ramp failure and 0.1 seconds for step failure.

关键词： Adaptation models Recurrent neural networks Atmospheric modeling Fault detection Transportation Benchmark testing Velocity measurement

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Relative Localization for UAV-UGV Based on Correlation of Bearing and Distance

Relative Localization for UAV-UGV Based on Correlation of Be...

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IEEE International Conference on Mechatronics and automation

作者： Jia Guo Kang Hu Jinting Liu State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

To ensure reliable localization for Unmanned Aerial Vehicles(UAVs) in the presence of uncertain speed of the Unmanned Ground Vehicle (UGV), this study examines the relative localization problem of UAV-UGV using distance and bearing measurements. A Correlation of Bearing and Distance-based Relative Localization (CBDRL) algorithm is proposed in this paper under this scenario. The estimation of altitude, distance, and angle are simplified into a representation of the relative positioning between the UAV and UGV. The relative height difference is measured using the barometer in the algorithm. To determine the relative distance, Time of Arrival (TOA) ranging and Ultra Wide Band (UWB) communication are utilized. The relative direction measurement is then determined using the correlations of bearing and distance. We integrate these observations with height, direction, and distance data in an Extended Kalman Filter(EKF) to provide accurate and reliable relative position estimates that allow the UAV to track the target. The simulation results indicate that the CBDRL method developed in this study is superior to previous relative localization algorithms that rely on multi-sensor fusion, and can significantly enhance the accuracy of UAV positioning provided that range and angle measurements are precise enough.

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Multi-UAV 3D Path Planning in Simultaneous Attack

Multi-UAV 3D Path Planning in Simultaneous Attack

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International Conference on control and automation (ICCA)

作者： Chuyi Xiong Bin Xin Miao Guo Yulong Ding Hao Zhang School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

As the key technology of UAV mission planning, path planning has a very important impact on the combat effectiveness of UAV. In order to destroy enemy units, multiple UAVs are often needed to attack the target at the same time. In the complex combat environment, the path planning of multiple UAVs is a very complex problem. On the basis of genetic algorithm, this paper designs a path planning algorithm to ensure that multiple UAVs arrive at the same time from different angles on the premise of satisfying environment constraints and the motion constraints of UAV. The algorithm uses the method of regionalization to initialize the path, designs a reasonable fitness function, and adds an adaptive disturbance operator to plan the path of each UAV. Finally, the length difference between the paths planned for each UAV is taken as the path evaluation factor. Some of the paths are replanned to achieve the goal of the same path length and simultaneous attack by UAV. The effectiveness of the algorithm is verified by experiments in typical combat scenarios, and the superiority of the adaptive disturbance operator in the optimization of the algorithm is verified by the contrast experiment.

关键词： Path planning Radar Planning Optimization Radar detection Genetic algorithms Three-dimensional displays

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Event-triggered Consensus control of Heterogeneous Multi-agent systems: Model- and Data-based Analysis

arXiv

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arXiv 2022年

作者： Wang, Xin Sun, Jian Wang, Gang Chen, Jie The School of Automation The Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing10081 China The Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China The Department of Control Science and Engineering Tongji University Shanghai201804 China The State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

This article deals with model- and data-based consensus control of heterogenous leader-following multi-agent systems (MASs) under an event-triggering transmission scheme. A dynamic periodic transmission protocol is developed to significantly alleviate the transmission frequency and computational burden, where the followers can interact locally with each other approaching the dynamics of the leader. Capitalizing on a discrete-time looped-functional, a model-based consensus condition for the closed-loop MASs is derived in form of linear matrix inequalities (LMIs), as well as a design method for obtaining the distributed controllers and event-triggering parameters. Upon collecting noise-corrupted state-input measurements during open-loop operation, a data-driven leader-following MAS representation is presented, and employed to solve the data-driven consensus control problem without requiring any knowledge of the agents' models. This result is then extended to the case of guaranteeing an H∞ performance. A simulation example is finally given to corroborate the efficacy of the proposed distributed event-triggering scheme in cutting off data transmissions and the data-driven design method. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear matrix inequalities

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Cross-Membrane Penetration to Nucleus of Adherent Cells Using Micropipettes Made by Borosilicate Glass and Quartz

Cross-Membrane Penetration to Nucleus of Adherent Cells Usin...

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IEEE International Conference on Mechatronics and automation

作者： Xinyue Xu Xiaoming Liu Yuqing Lin Pengyun Li Fengyu Liu Xiaoqing Tang Qiang Huang Tatsuo Arai Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In the biomicromanipulation field, single-cell manipulation is receiving attention due to its potential applications in many aspects of biomedicine. Cell injection, as a typical cell micromanipulation technique, is devoted to introducing foreign substances into living cells by penetrating a fine micropipette into cell membranes. Direct access to the intracellular environment, especially the nucleus, provides an ideal way to gain insight into the dynamic processes of living cells. This paper presents the comparison of controlled penetrations of two types of adherent cells and their nuclei by two types of micropipettes, made of borosilicate glass and quartz respectively. Due to the differences in the cell properties (e.g., height, adhesion strength), two corresponding penetration strategies optimizing the puncture angle and puncture height, are proposed to improve the success rate of the puncture process and the survival rate of cells after the puncture. Real nucleus puncture experiments adopting the optimal puncture strategy for each type of cells and carried out by a semi-automatic system, indicated a better puncture effect by quartz pipettes. Through this method, functional materials such as peptides and nanoparticles can be potentially introduced to perturb the cell activities and then realize its full automation.

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Convolutional Neural Network Features Comparison Between Back-Propagation and Extreme Learning Machine

Convolutional Neural Network Features Comparison Between Bac...

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Chinese control Conference

作者： Atmane Khellal Hongbin Ma Qing Fei School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

Recently deep learning based architectures have been widely deployed in many problems of artificial intelligence. Among deep learning models, Convolutional Neural Networks (CNN) have been reported in numerous successful applications such as object recognition, and natural language processing. The convolutional neural networks are trained by back-propagating the classification error using the Back-Propagation (BP) algorithm, which requires a large amount of data and slows the training process. To overcome these difficulties, a new fast and accurate approach based on Extreme Learning Machine (ELM) to train any convolutional neural network has been proposed. The developed framework (ELM-CNN) is based on the concept of auto-encoding to learn the convolutional filters with biases, by reconstructing the normalized input and the intercept term. In this paper, systematic comparison with traditional back-propagation based training method (BP-CNN) has been made with respect to two aspects qualitative and quantitative. The experimental results on the popular MNIST dataset show that the ELM-CNN algorithm achieves competitive results in terms of generalization performance and up to 16 times faster than the back-propagation based training of CNN.

关键词： Training Convolutional neural networks Feature extraction Computer architecture Convolution Machine learning Encoding

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