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Industrial Cyber-Physical systems (ICPS)

作者： Jiahe Cai Sheng Du Chengda Lu Bo Xiao Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Guangzhou Marine Geological Survey Guangzhou China

With the development of unmanned surface vessel in the world today, obstacle avoidance using environmental information is the basis to ensure its high maneuvering performance and safety. However, directly using standard algorithms will lead to missing and wrong identification severely for characteristics of marine obstacles. This paper adds a multi-scale feature extraction layer of dilation convolution and group convolution to Faster Region based Convolutional Neural Network (Faster-RCNN), the baseline model, and changes the classification algorithm to improve its robustness and accuracy. Soft-Non Maximum Suppression (Soft-NMS) is used to enhance the prediction effects further. After improvements, the mean average precision value increases by 3.35%, and the final loss value decreases by 0.20. Given the phenomenon of missing and misidentification in the prediction by the baseline model, the results of our new model show outstanding performance.

关键词： Convolution Predictive models Feature extraction Prediction algorithms Robustness Classification algorithms Safety

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Photovoltaic Data Cleaning Method Based on DBSCAN Clustering, Quartile Algorithm and Pearson Correlation Coefficient Interpolation Method

Photovoltaic Data Cleaning Method Based on DBSCAN Clustering...

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Energy, Electrical and Power Engineering (CEEPE), International Conference on

作者： Biyuan Wang Xingye Deng Taowei Chen Yidong Li Huizhou Power Supply Bureau Guangdong Power Grid Co Guangdong China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China

The proportion of photovoltaic power generation in the global energy structure is increasing year by year. Because of its volatility and periodicity, grid-connected photovoltaic challenges the safe and stable operation of power grid. One of the ways to solve this problem is data mining. Data mining needs high-quality data support, and data cleaning is one of the important means to improve data quality. Aiming at the problem of poor quality of original PV data, this paper analyzes two typical outliers of PV data and proposes a PV output data cleaning method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm, quartile algorithm and Pearson correlation coefficient interpolation method. Comparison of DBSCAN clustering algorithm combined with the quartile method of outlier identification with the quartile method alone outlier identification, the effectiveness of the method proposed in this paper is verified. Then using the method proposed in this paper for outlier identification based on Pearson correlation coefficient interpolation and cubic spline interpolation to verify the data filling effect, the results show that Pearson correlation coefficient interpolation is superior to cubic spline interpolation. Finally, two sets of data with different data cleaning methods are substituted into the Long short-term memory (LSTM) Model to verify accuracy of the cleaning method proposed in this paper.

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Fixed-Time Sliding Mode Tracking control for Two-Dimensional Directional Drilling System

Fixed-Time Sliding Mode Tracking Control for Two-Dimensional...

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Asian control Conference

作者： Jinyu Li Hongchao Wei Chengda Lu Wangnian Li Quanxin Li Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, the effect of the initial state of the drilling system on trajectory tracking is taken into account in the directional drilling trajectory tracking control. Firstly, the trajectory model describing the two-dimensional drilling trajectory of the rotary steering system (RSS) is introduced. To improve the accuracy of trajectory tracking control and reduce the trajectory deviation, a fixed-time sliding mode control (FTSMC) method is introduced. Then, the stability of the closed-loop system is analyzed, and the range of controller parameters satisfying the stability condition is obtained. Finally, the effectiveness of the designed control strategy is verified by simulation experiments.

关键词： Drilling Upper bound Trajectory tracking Steering systems Stability analysis Trajectory Closed loop systems

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A Trajectory Tracking Method using Dynamic Sliding Mode control with Parameter Optimization for Autonomous Underwater Vehicles

A Trajectory Tracking Method using Dynamic Sliding Mode Cont...

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Industrial Cyber-Physical systems (ICPS)

作者： Weiliang Li Xuzhi Lai Sheng Du Chengda Lu Yawu Wang Zonghuan Chen Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Guangzhou Marine Geological Survey Guangzhou China

Autonomous underwater vehicles (AUVs) must be able to track a particular trajectory when performing various tasks. Sometimes, the widely used control method in actual marine engineering, the proportional integral derivative (PID) control method cannot meet the accuracy requirements of AUVs' trajectory tracking. In this paper, a dynamic surface sliding mode controller for trajectory tracking is designed. A nonlinear disturbance observer is used to compensate for environmental interference, and an improved particle swarm optimization with dynamic inertia weight is applied to optimize the control parameter. Simulation experiments are based on the mathematical model of an underwater robot BLUEROV2, and the control and optimization algorithms are designed.

关键词： Autonomous underwater vehicles Trajectory tracking Heuristic algorithms Mathematical models Trajectory Vehicle dynamics Particle swarm optimization

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Distributed Active Disturbance Rejection control of Multiple Mobile Robots for Online Workload Partition

Distributed Active Disturbance Rejection Control of Multiple...

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Chinese automation Congress (CAC)

作者： Xin Li Chao Zhai Hehong Zhang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan Campus School of Automation Wuhan China School of Computer and Data Science Fuzhou University Fuzhou China

Considering the actual multi-agent coverage process, the motion trajectories are seriously affected by disturbances and noise. In this paper, a cooperative control method based on active disturbance rejection controller (ADRC) for multiple mecanum-wheeled robots (MWMR) is proposed to improve its robustness. In particular, to suppress the total disturbances during the running of each robot and reduce large computation and observation cost of high order ADRC, a reduced-order cascaded ADRC (RC-ADRC) is proposed. Moreover, distributed controller is proposed to track the expected trajectories of multiple mecanum-wheeled robots under disturbances. Compared with the proposed RC-ADRC based cooperative control scheme and PID based cooperative control one, the favorable motion trajectory tracking performance is obtained. Simulation results verify the superiority of the method.

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Fast ADMM algorithm with efficient communication for distributed optimization of multi-agent systems

Fast ADMM algorithm with efficient communication for distrib...

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2021 China automation Congress, CAC 2021

作者： Cao, Shuang Jiang, Xiaowei Chi, Ming Zhang, Xianhe School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Ministry of Education Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9781665426473

This paper proposes a distributed optimization algorithm based on alternating direction method of multipliers (ADMM) for the distributed optimization problem of multi-agent systems, called ADMM with adaptive penalty terms and conditional communication (for short, AAPCC), by using the local information of agents to update the adaptive penalty terms to speed up the convergence speed while by restricting the communication conditions of agents to reduce the communication cost of agents in the iterative process. Thus, the communication cost of the system is reduced while the convergence speed is ensured. Numerical simulation experiments show that in a multi-agent system, compared with the classical ADMM, AAPCC can better balance the convergence speed and communication cost, suitable for multi-agent systems that require fast convergence speed and less communication cost. © 2021 IEEE

关键词： Multi agent systems

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Lane-Change Intention Prediction of Surrounding Vehicles Using BiLSTM-CRF Models with Rule Embedding

Lane-Change Intention Prediction of Surrounding Vehicles Usi...

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Chinese automation Congress (CAC)

作者： Kai Wang Jie Hou Xianlin Zeng Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Predicting lane-change intentions of surrounding vehicles can effectively help autonomous vehicles reduce collisions caused by lane changes and ensure driving safety. Because prediction methods based on black-box models will lead to passengers’ distrust of machine prediction, the intention prediction methods used to autonomous driving need to be interpretable and trustworthy. This paper presents a method for intention prediction of surrounding vehicles by using a bidirectional long short term memory network (BiLSTM) with a conditional random field (CRF) layer above it. Compared with intention prediction methods using deep network, the proposed method can find the features that contribute most to the prediction, thereby improving the interpretability and ensuring the prediction performance. In addition, by employing the transfer characteristic of the CRF layer, traffic rules and the experience of skilled drivers can be embedded to the prediction in the form of rules. Use rules to constrain the intention prediction, thereby improving the trustworthiness of prediction results. Test results on naturalistic driving dataset show that the proposed method can predict the lane-change intention with an accuracy of 97.22%, which is higher than that of Bi-LSTM.

关键词： Training Road transportation Knowledge engineering Predictive models Safety Autonomous vehicles Standards

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Can Digital Intelligence and Cyber-Physical-Social systems Achieve Global Food Security and Sustainability?

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2070-2080页

作者： Yanfen Wang Mengzhen Kang Yali Liu Juanjuan Li Kai Xue Xiujuan Wang Jianqing Du Yonglin Tian Qinghua Ni Fei-Yue Wang College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049 Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408 State Key Laboratory of Tibetan Plateau Earth System Environment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Grassland Science Beijing Forestry UniversityBeijing 100083China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China State Key Laboratory of Plateau Ecology and Agriculture Qinghai UniversityXining 810016 Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau BiologyChinese Academy of SciencesXining 810001 Binzhou Institute of Technology Weiqiao-UCAS Science and Technology ParkBinzhou 256606China Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408China Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078 State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078 Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China IEEE

Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration *** the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability. Digital intelligence(DI) and cyber-physical-social systems(CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence(AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semiarid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the

关键词： Carbon-water balance decision-support digital intelligence(DI) foundation models planning

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Multisensor fusion estimation of nonlinear systems with intermittent observations and heavy-tailed noises

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Science China(Information Sciences) 2022年第09期65卷 173-190页

作者： Bo XIAO Q.M.Jonathan WU Liping YAN School of Artificial Intelligence Beijing University of Posts and Telecommunications Department of Electrical and Computer Engineering University of Windsor Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Inspired by the robust student t-distribution based nonlinear filter(RSTNF), a student tdistribution and unscented transform(UT) based filter for state estimation of heavy-tailed nonlinear dynamic systems, a modified RSTNF for intermittent observations is derived. The fusion estimation for nonlinear multisensor systems with intermittent observations and heavy-tailed measurement and process noises is *** this work, the centralized fusion, the sequential fusion, and the na¨?ve distributed fusion algorithms are presented, respectively. Theoretical analysis shows that the presented algorithms are effective, which are the efficient extension of the classical unscented Kalman filter(UKF) or the cubature Kalman filter(CKF) based algorithms with Gaussian noises. Simulation results show that the presented algorithms are effective and feasible.

关键词： state fusion estimation nonlinear systems heavy-tailed noise intermittent observations multivariate t-distribution

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Wearable ubiquitous energy system

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Science China(Information Sciences) 2021年第2期64卷 237-239页

作者： Fang DENG Ning DING Ziman YE Yeyun CAI Jie CHEN School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

In recent years, owing to the growing maturity of Internet of Things technology and mobile communication technology, intelligent wearable devices have developed *** have been widely applied in aviation, medicine, military, entertainment and other fields. Through intelligent wearable devices, people can communicate efficiently, dynamically perceive external environments, and monitor the body's vital signs.

关键词： Information systems and Communication Service

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