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A practical PID variable stiffness control and its enhancement for compliant force-tracking interactions with unknown environments

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science China(Technological sciences) 2023年第10期66卷 2882-2896页

作者： ZHANG Xin ZHOU Hao LIU JinGuo JU ZhaoJie LENG YuQuan YANG ChenGuang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China School of Computing University of PortsmouthPortsmouth PO13HEUK Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhen 518055China Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Compliant interaction control is a key technology for robots performing contact-rich manipulation *** design of the compliant controller needs to consider the robot hardware because complex control algorithms may not be compatible with the hardware performance,especially for some industrial robots with low bandwidth *** paper focuses on effective and easy-to-use compliant control algorithms for position/velocity-controlled *** by human arm stiffness adaptation behavior,a novel variable target stiffness(NVTS)admittance control strategy is proposed for adaptive force tracking,in which a proportional integral derivative(PID)variable stiffness law is designed to update the stiffness coefficient of the admittance function by the force and position ***,its stability and force-tracking capability are theoretically *** addition,an impact compensator(Impc)is integrated into the NVTS controller to enhance its disturbance-suppression capability when the robot is subjected to strong vibration disturbances in complicated surface polishing *** proposed controllers are validated through four groups of experimental tests using different robots and the corresponding results demonstrate that they have high-accuracy tracking capability and strong adaptability in unknown environments.

关键词： compliant interaction control force tracking PID variable stiffness impact compensator unknown environments

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Detection Algorithm of Gastric Mucosal Atrophic Lesions under Gastroscopy Based on Deep Learning

Detection Algorithm of Gastric Mucosal Atrophic Lesions unde...

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第35届中国过程控制会议

作者： Yining Zhao Xiaolei Guo Xinao Jin Yuqi Cao Guangxin Zhang Yuehua Han State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Department of Gastroenterology the Second Affiliated Hospital Zhejiang University School of Medicine

Autoimmune gastritis(AIG) is a type of atrophic gastritis that is closely related to diseases including pernicious anemia, gastric adenocarcinoma and gastric neuroendocrine tumors. Gastroscopy is one of the most important tools for the diagnosis of AIG, and the most important lesion of AIG under gastroscopy is the high atrophy of the gastric mucosa, mainly in the body and fundus of stomach. However, the large number of gastroscopy images that need to be interpreted brings a large workload to doctors, which is not conducive to the efficient and accurate diagnosis of AIG. Therefore, we propose the multiscale Yolov5l(MS-Yolov5l) for gastric mucosal atrophic lesions to realize automated assisted diagnosis of AIG. For the problems of uneven brightness and noise in gastroscopy images, CLAHE and Gaussian filter are used to process the images. Aiming at the multi-scale characteristics of gastric mucosal atrophy lesions, DAC block is used to improve the extraction ability of the model for multi-scale information. For the detection of gastric mucosal atrophy lesions, we obtained an AP value of 0.744 and a recall value of 0.950. The study shows that the detection algorithm based on deep learning is capable of extracting gastric mucosal atrophic lesions in gastroscopy images, which is of great practical value for the further development of automated diagnosis and auxiliary diagnosis for AIG.

关键词： Medical Images Processing Autoimmune Gastritis Lesion Detection Deep Learning

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Observer-based event-triggered control for linear MASs under a directed graph and DoS attacks

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Journal of control and Decision 2022年第3期9卷 384-396页

作者： Shuo-Qiu Zhang Wei-Wei Che Chao Deng College of the Institute of Complexity Science Shandong Key Laboratory of Industrial Control TechnologyQingdao UniversityQingdaoPeople’s Republic of China Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjingPeople’s Republic of China

In this paper,we investigate the observer-based event-triggered consensus problem for linear multi-agent systems(MASs)under a directed graph and denial-of-service(DoS)attacks.A type of DoS attacks launched by malicious attackers at irregular intervals is considered,which can cause communication channel disruption.A novel event-triggered secure control scheme based on a closed-loop observer is proposed to determine the scheduling of the controller update,and a separation method with less conservativeness is employed to design the controller and observer ***,the frequency and duration of DoS attacks that can be tolerated are analysed for the observer-based secure consensus *** addition,a strictly positive minimal event-triggered time interval for each agent is designed with the help of the proposed eventtriggered condition to eliminate the Zeno ***,a numerical simulation is given to verify the theoretical analysis.

关键词： Directed graph DoS attacks event-triggered mechanism consensus control multi-agent systems

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Multi-Robot Path Planning in Narrow Warehouse Environments with Fast Feasibility Heuristics 41

Multi-Robot Path Planning in Narrow Warehouse Environments w...

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第41届中国控制会议

作者： Jiaxi Huo Ronghao Zheng Senlin Zhang Meiqin Liu College of Electrical Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Zhejiang University Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we present a novel method for solving the multi-robot path planning problem in narrow warehouse *** may have to be operated in close proximity to each other in narrow lanes between pod clusters,which challenges robot-robot collision *** motivates us to consider an efficient algorithm to generate collision-free robot paths in narrow warehouse *** algorithm builds a two-stage scheme based on integer programming:(i) multirobot path planning under one-way constraints;and(ii) fast feasibility *** developed algorithm has two advantages:(i) the proposed scheme successfully avoids the situation where the robots collide in narrow lanes;and(ii) by using the fast feasibility heuristics which replaces the integer programming solver's built-in feasibility heuristics,the feasibility heuristics' running time can be effectively reduced while producing better initial feasible solutions for the integer programming model solving *** with the existing optimal,sub-optimal,and polynomial-complexity algorithms,our developed algorithm can leverage the advantage of integer programming to effectively reach a balance between running time and optimality in narrow warehouse *** point is demonstrated in the simulations.

关键词： Path planning Multi-robot or multi-agent systems Collision avoidance Logistics

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Multi-component quantitative and feed-forward neural network for pattern classification of raw and wine-processed Corni Fructus

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Traditional Medicine Research 2023年第1期8卷 12-19页

作者： Yu Liu Ying-Fang Cui Dan-Dan Shi Shu-Li Man Xia Li Wen-Yuan Gao State Key Laboratory of Food Nutrition and Safety/Key Laboratory of Industrial Microbiology Ministry of Education/Tianjin Key Laboratory of Industry Microbiology/National and Local United Engineering Lab of Metabolic Control Fermentation Technology/China International Science and Technology Cooperation Base of Food NutritionSafety and Medicinal Chemistry/College of BiotechnologyTianjin University of Science&TechnologyTianjin 300457China Tianjin Key Laboratory for Modern Drug Delivery&High-Efficiency School of Pharmaceutical Science and TechnologyTianjin UniversityTianjin 300072China.

Background:To promote the quality evaluation,clarify the processing mechanism and distinguish origins of Corni Fructus(cornus)from different ***:This study developed a high performance liquid chromatography method for simultaneous determination of 5-hydroxymethylfurfural,2 phenolic acids and 4 iridoid glycosides and the reference fingerprint of cornus from different *** addition,the feedforward neural network model provided a pattern classification of sample ***:The content of morroniside and loganin were the highest in all raw cornus samples ranging from 9.45μg/mg to 16.3μg/mg and 6.64μg/mg to 13.7μg/mg,*** level of sweroside in raw cornus from Henan(0.83μg/mg^(-1).39μg/mg)and Zhejiang(0.64μg/mg^(-1).17μg/mg)were greater than other *** wine-processing,the glucose or fructose were dehydrated to increase the levels of *** C-4 position of-COOCH3 of hot-sensitive iridoid glycosides was hydrolyzed to generate-COOH as stable *** derivatives may be degraded to increase the content of phenolic ***,an excellent feedforward neural network model for identification of raw cornus and wine-prepared cornus was established which could distinguish the sample ***:This work provided a trustworthy method to evaluate the quality and distinguish the sources of ***,the clear processing mechanism provided a scientific foundation for controlling the cornus quality during wine-processing.

关键词： Cornus officinalis Sieb.et Zucc. quality evaluation fingerprints processing mechanism feedforward neural network

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A Sequentially-Adaptive Deep Variational Model for Multirate Process Anomaly Detection 3

A Sequentially-Adaptive Deep Variational Model for Multirate...

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3rd International Conference on industrial Artificial Intelligence, IAI 2021

作者： Chai, Zheng Zhao, Chunhui Sun, Youxian Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China

ISBN: (纸本)9781665435178

Deep learning based process monitoring methods are attracting increasing research attention in recent years, which generally assume that the process variables are uniformly sampled. In practice, however, the process data are generally collected at multiple different rates, resulting in structurally-incomplete training data. Under such circumstances, how to build effective deep models to fully mine the multirate sampled data has become a constraint in achieving better process monitoring performance. In this paper, a sequentially-adaptive deep variational model is designed in which the knowledge that existed in variables with different rates is comprehensively extracted through deep generative neural networks. The multirate samples are first divided into multiple data blocks in which each block is collected at a uniform rate. A deep generative model is then constructed to model the uncertain data distribution and extract probabilistic feature representations considering the slowness principle. To restrain the small data problem in the blocks with slow rates, a sequentially-adaptation strategy is designed to adapt the knowledge from the fast blocks with sufficient training data and enhance the overall modeling performance. The effectiveness is demonstrated through a real-world industrial thermal power plant case. © 2021 IEEE.

关键词： Process monitoring

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A Novel Optimization Method for Raw Coal Blending in Coal Preparation Plants Based on Interval Programming

A Novel Optimization Method for Raw Coal Blending in Coal Pr...

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2023 China Automation Congress, CAC 2023

作者： Lei, Jiahui Liu, Hanwen Zhao, Jun Wang, Wei Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China Chn Energy Wuda Coal Processing Co. Ltd. Wuhai China

ISBN: (纸本)9798350303759

In the process of coal blending and washing in coal preparation plants, there are various raw coals with different quality that changes greatly, which results in poor quality and low profit. To solve these problems, a novel optimization method for raw coal blending based on interval programming is proposed. Interval numbers are introduced to represent the fluctuation range of ash and sulfur content at each density level of raw coals. A predictive model of quantity and quality of dense medium cyclone products is constructed, which uses Hermite interpolation to obtain washability curves of each raw coal and adopts fitting methods to obtain the distribution curve. In order to obtain optimal coal blending schemes, considering operation and maintenance costs of raw coal feeding line, a mixed integer nonlinear programming (MINLP) model is built, whose objective is to maximize profit with the main constraints of clean coal quality and inventory of raw coal. Interval possibility is introduced to transform constraints with interval number and a customized branch and bound algorithm is used to solve it. In order to verify the effectiveness of the method in this paper, experiments are conducted using manufacturing data from a domestic coal preparation plant. The experimental results indicate that the proposed method is practical and has better economic performance. © 2023 IEEE.

关键词： Profitability

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A Generalized Wind Turbine Anomaly Detection Method Based on Combined Probability Estimation Model

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1136-1148页

作者： Xiangjun Zeng Ming Yang Chen Feng Yaohua Tang Key Laboratory of Power System Intelligent Dispatch and Control Shandong UniversityJinan 250061Chinaand X.Zeng is also with the College of Electrical Engineering&New EnergyChina Three Gorges UniversityYichang 443002China Rundian Energy Science and Technology Co. Ltd.Zhengzhou 450046China

Anomaly detection based on the data collected from the supervisory control and data acquisition(SCADA)system is crucial to reduce the failure rate of wind turbines(WTs).The difficulty of this kind of methods is to dynamically identify the threshold for anomaly detection under changing operating *** this paper,a generalized WT anomaly detection method based on the combined probability estimation model(CPEM)is *** CPEM can estimate the conditional probability density function(PDF)of the target variable given changing *** generalization and accuracy are better than those of the independent probability estimation model because it combines the advantages of various kinds of probability estimation models through linear *** using the CPEM,the normal operating bounds under different operating conditions can be obtained,which dynamically form the thresholds for anomaly ***,with respect to the thresholds,hypothesis testing(HT)is adopted to identify the anomaly by inspecting whether the observations exceed the thresholds at a given significance level,providing sound mathematical support for anomaly detection and making the detection results more *** effectiveness of the proposed method is tested by using the actual data of WTs with known *** results illustrate that the proposed method can detect the abnormal operating state of the gearbox and generator much more early than the system fault alarm.

关键词： Anomaly detection combined probability estimation generalization hypothesis testing(HT)

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Dynamic Multivariate Alarm Threshold Optimization for Nonstationary Processes Subject to Varying Conditions 10

Dynamic Multivariate Alarm Threshold Optimization for Nonsta...

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10th IEEE Data Driven control and Learning Systems Conference, DDCLS 2021

作者： Zhao, Yi Zhao, Chunhui Sun, Youxian State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665424233

For nonstationary processes working upon varying conditions, the conventional alarm thresholds such as 3sigma rules configured for one single operational zone often result in false and missed alarms. Besides, the interactions among process variables are also isolated. To address these issues, this paper proposes a novel dynamic multivariate alarm threshold optimization algorithm. Firstly, the process correlation variations can be identified by Toeplitz inverse covariance-based clustering method, pointing to the changes of operating conditions. Each condition is structural interpreted by a covariance inverse matrix of the multivariate Gaussian distribution, revealing similar within-time and cross-time variable interactions. Therefore, it provides a promising foundation to capture the conditional Gaussian distribution and design the corresponding thresholds of each variable, which finely covers the current normal operational zone. Then, offline threshold optimization and online strategy are developed and discussed in details, which can timely adapt to varying conditions, promoting accurate and sensitive alarming performance. Finally, the experimental results on a real thermal power plant show that the proposed threshold can ease operators from nuisance, thus improving the reliability of alarm system. © 2021 IEEE.

关键词： Inverse problems

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Impact of damming on nutrient transport and transformation in river systems: A review

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Water science and engineering 2025年第2期18卷 209-220页

作者： Gan, Jia-wen Wang, Xun Yuan, Qiu-sheng Xing, Xiao-lei Liu, Sheng Du, Cheng-gong Zheng, Yu-ran Liu, Yun-xin Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes of Ministry of Education Hohai University Nanjing210098 China College of Environment Hohai University Nanjing210098 China Faculty of Environmental Science and Engineering Kunming University of Science and Technology Kunming650500 China Yunnan Provincial Key Laboratory of Carbon Sequestration and Pollution Control in Soils Kunming650500 China School of Civil Engineering Shandong University Jinan250061 China School of Life Science Huaiyin Normal University Huai'an223300 China Jiangsu Key Laboratory for Eco-Agricultural Biotechnology Around Hongze Lake Huai'an223300 China

Large-scale damming has emerged as a prevalent global trend, significantly impacting nutrient transport and transformation, as well as the downstream ecological environment. Nitrogen and phosphorus are fundamental elements of primary productivity in aquatic ecosystems and serve as key limiting factors in reservoir eutrophication. This review focuses on the impact of damming on the transport and transformation of nitrogen and phosphorus, regarding changes in nutrient concentrations, fluxes, and proportions. Spatial changes in nitrogen and phosphorus concentrations primarily occur at the inlet and outlet of reservoirs, while temporal changes often exhibit seasonal patterns. At a global scale, phosphorus is preferentially removed from reservoirs compared to nitrogen. The factors influencing the transport and transformation processes of nitrogen and phosphorus in reservoirs include the physicochemical properties of water bodies and human activities. Additionally, nitrogen dynamics are affected by reservoir age, storage capacity, and water storage regulation modes, whereas phosphorus dynamics are also influenced by hydrodynamic conditions. Finally, this review summarizes the impact of damming on the downstream ecological environment and outlines future research directions, providing theoretical support for the management of river–reservoir ecosystems and promoting the green and sustainable development of hydropower in the context of carbon peaking and carbon neutrality goals. © 2024 Hohai University

关键词： Eutrophication

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