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5th International Conference on Robotics, control and automation engineering, RCAE 2022

作者： Mao, Jizhi Yang, Zhijia Zhang, Shusheng College of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9781665459419

The model design and basic principle of liquid level control system are described. In order to improve the dynamic response of liquid level control, the overshoot and steady-state error accuracy of the system are improved. In this paper, we apply the conventional PID controller to the single-loop system and the cascade system respectively, and introduce the fuzzy control algorithm based on the conventional PID control algorithm to design a cascade parameter self-adjusting fuzzy PID controller, Then use genetic algorithm to optimize fuzzy control rules to generate genetic fuzzy PID controller. The MATLAB/Simulink simulation results show that, compared with the traditional PID control, the genetic fuzzy PID control has better control effect and anti-interference ability, and has good control performance. © 2022 IEEE.

关键词： Three term control systems

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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Multi-Feature Fusion Transformer for Natural Language Inference

Multi-Feature Fusion Transformer for Natural Language Infere...

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Robotics, Intelligent control and Artificial Intelligence (RICAI), International Conference on

作者： Lei Sun Xiangyang Li Hengxin Yan Tianj in Key Laboratory for Control Theory and Complicated Industry Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

Natural Language Inference (NLI) is a branch of Natural Language Processing (NLP) whose main task is to determine the relationship between two sentences. Such tasks essentially use pre-trained models to ensure accuracy, and many applications are found in human-computer dialog and question-answering systems. However, in some cases where small devices are deployed offline, lightweight model implementations are often required to save computational resources. To address this problem and ensure more efficient inference capabilities, this paper describes how to improve the preexisting Transformer and proposes a Multi-Feature Fusion Transformer Network for NLI processing. The model integrates sequence features and introduces a priori information to enhance local features. It also achieves a comprehensive fusion of sequence information, local features, and non-local features, thus compensating for many potential shortcomings of Transformer. At the same time, it maintains the lightweight feature of the model and facilitates the implementation of downstream tasks. We verified the above point by experiments, our model has better inference performance compared to previous models used for the same task. The accuracy in the SNLI dataset reached 89.0%, while the matched and mismatched versions of MultiNLI reached 79.3% and 78.7%.

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Iterative assist-as-needed control with interaction factor for rehabilitation robots

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Science China(Technological Sciences) 2021年第4期64卷 836-846页

作者： CAO Ran CHENG Long YANG ChenGuang DONG Zhe School of Artificial Intelligence University'of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Institute of Automation.Chinese Academy of SciencesBeijing 100190China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China School of Electrical and Control Engineering North China Universityof TechnologyBeijing 100144China

Rehabilitation robots for stroke patients have drawn considerable attention because they can reduce the economic and labor costs brought by traditional rehabilitation. control methods for rehabilitation robots have been developed to stimulate the active motion of patients and to improve the effectiveness of rehabilitation care. However, current control methods can only roughly adjust the system’s stiffness and may fail in achieving satisfactory performance. To this end, this paper introduces a novel cost function consisting of the tracking error term and the stiffness term. The cost function contains an interaction factor that represents the patient’s motion intention to balance the weight of these two terms. When the patients try to actively do training tasks, the weight of stiffness term increases, which leads to the larger allowable tracking error and lower stiffness eventually. An iterative updating law of the stiffness matrix is given to reduce the proposed cost function. Theoretical analysis based on the Lyapunov theory is given to ensure the feasibility of the proposed algorithm. Furthermore, a force estimation is used to improve interaction control performance. Finally, simulation experiments are provided to show the effectiveness of the proposed algorithm.

关键词： human-robot interaction impedance stiffness control rehabilitation

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Social relation and physical lane aggregator:integrating social and physical features for multimodal motion prediction

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Journal of Intelligent and Connected Vehicles 2022年第3期5卷 302-308页

作者： Qiyuan Chen Zebing Wei Xiao Wang Lingxi Li Yisheng Lv The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIndianaUSA Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina and School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina

Purpose–The purpose of this paper aims to model interaction relationship of traffic agents for motion prediction,which is critical for autonomous *** is obvious that traffic agents’trajectories are influenced by physical lane rules and agents’social ***/methodology/approach–In this paper,the authors propose the social relation and physical lane aggregator for multimodal motion prediction,where the social relations of agents are mainly captured with graph convolutional networks and self-attention mechanism and then fused with the physical lane via the self-attention ***–The proposed methods are evaluated on the Waymo Open Motion Dataset,and the results show the effectiveness of the proposed two feature aggregation modules for trajectory ***/value–This paper proposes a new design method to extract traffic interactions,and the attention mechanism is used in each part of the model to extract and fuse different relational features,which is different from other methods and improves the accuracy of the LSTM-based trajectory prediction method.

关键词： Deep learning Machine learning Autonomous driving Trajectory prediction

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Robust Dynamic Latent Variable Model for Dynamic Process Monitoring With Missing Data in Cyber-Physical systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2023年 1卷 227-235页

作者： Le Zhou Lixinqian Yu Beiping Hou Hongbo Zheng Zheng-Guang Wu School of Automation and Electrical Engineering Zhejiang University of Science & Technology Hangzhou China Faculty of Engineering and IT University of Melbourne Melbourne Australia National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

Although remarkable studies on dynamic process modeling and monitoring in cyber-physical systems have been conducted, there are still several limitations to these approaches, rendering them unsuitable for practical applications. Firstly, the common dynamic models are built based on complete data, which means that their modeling performance usually deteriorates when there are missing measurements. Secondly, practical data often exhibits more complex characteristics, such as non-stationary and non-Gaussian noises. Therefore, a robust dynamic latent variable model is proposed to address the challenges of dynamic process modeling and monitoring in complex measurement environments with missing values. The proposed method effectively handles non-Gaussian noises and recursively estimates missing values, thus enhancing the monitoring performance within the dynamic latent variable modeling framework. Finally, the feasibility and superiority of the proposed method are evaluated through a numerical example and a real wastewater treatment case, demonstrating its effectiveness and practicality in real-world scenarios.

关键词： Cyber-physical systems Principal component analysis Process monitoring Data models Load modeling Numerical models Loading

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Development and Optimization of a WebGL-Based Mechanical Model Simulation Platform 4th

Development and Optimization of a WebGL-Based Mechanical Mod...

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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Wang, Xintong Tian, Hao Fang, Jingzhe Zhang, Hualiang Zhang, Tao Software College Shenyang University of Technology Liaoning Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Electrical Engineering Shenyang University of Technology Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9789819778867

The need for mechanical model simulation platforms is growing as a result of the ongoing advancements in digital technology and the mechanical manufacturing sector. The application scope and efficacy of simulation platforms are severely limited by the issue of sluggish rendering performance, which frequently arises when obtaining high-quality rendering. When drawing high demand mechanical models, a lot of processing power is frequently needed, which makes real-time rendering speed unsatisfactory for practical purposes. In order to achieve real-time rendering and simulation of 3D robots, this experiment built and implemented a WebGL-based 3D simulation platform. Additionally, the simulation platform can be optimized by increasing frame rate and rendering speed without sacrificing rendering effect by building a mixed bounding box. © The Author(s) 2025.

关键词： Digital elevation model

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Hybrid Robust Sliding Mode and PI-controller for Stand-Alone Object’s Power Supply 25

Hybrid Robust Sliding Mode and PI-Controller for Stand-Alone...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Lyapunov, Danil Y. Fedyanin, Alexandr L. Pham, Trong H. Pravikova, Alexandra A. Shilin, Aleksandr A. Kremzukov, Yuriy A. Division for Power and Electrical Engineering National Research Tomsk Polytechnic University Tomsk Russia Seversk Technological Institute - branch of State Autonomous Educational Institution of Higher Education National Research Nuclear National University MEPhi Seversk Russia Research Institute of Automation and Electromechanics Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

ISBN: (纸本)9798350389234

This paper delves into the problem of robust control of a power supply for a stand-alone remotely operated object, having power equipment onboard. Such objects are extensively used in oil and gas exploration, their production, offshore research and related problem areas. Power equipment generally involves both lighting and electric drives to provide remote operation of manipulators, thrusters and other electric components. Providing stable voltage, delivered to the object via long cables, represents an up-to-date problem for power supply developers. Research efforts, outlined in the present paper, suggest one of possible solution of this problem, namely, involving hybrid robust controller. This controller combines both sliding mode and conventional PI controllers in its structure. Such a solution provides not only stable voltage within the error band of ±1% with respect to a rated value of 600 V but also voltage surge mitigation when electric load changes stepwise. The voltage ripple ratio does not exceed 0.5%. Matlab Simulink model of the power supply confirmes t he a pplicability o f the designed hybrid controler. © 2024 IEEE.

关键词： Robust control

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ADAPTIVE ITERATIVE LEARNING TORQUE control OF ELECTRO-HYDRAULIC LOAD SIMULATOR

ADAPTIVE ITERATIVE LEARNING TORQUE CONTROL OF ELECTRO-HYDRAU...

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2024 CSAA/IET International Conference on Aircraft Utility systems, AUS 2024

作者： Shu, Sheng Wu, Shuai School of Automation Science and Electrical Engineering Beihang University Beijing China National Key Laboratory of Aircraft Integrated Flight Control Beihang University Beijing China Key Laboratory of Advanced Aircraft Systems Beihang University Ministry of Industry and Information Technology Beijing China Ningbo Institute of Technology Beihang University Ningbo China

The electro-hydraulic load simulator which can simulate the load executed on the aircraft actuator in the process of flight is an important ground test equipment. The uncertainties of the system including the disturbances caused by the motion of actuator make it challenging to achieve high performance. This study develops an adaptive iterative learning controller for the torque control of electro-hydraulic load simulator utilizing the backstepping method. On the basis of adaptive backstepping control, this controller incorporates iterative learning law to learn unknown nonlinear functions. By utilizing the repetitive characteristic of the test equipment, the iterative learning law sacrifices learning time to achieve enhanced control performance. To handle unknown disturbances, filter error, and learning error, a robust term described by dynamic equations is constructed. The calculation of the derivative of the virtual control law is simplified with a first-order command filter. Simulations with three different controllers are obtained to illustrate the learning ability of the proposed controller. © The Institution of engineering & Technology 2024.

关键词： Backstepping

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Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1990-2004页

作者： Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the China Electric Power Research Institute Beijing 100192China IEEE the Qingdao Academy of Intelligent Industries Qingdao 256200China the School of Information Science and Technology Nantong UniversityNantong 226019China the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based *** recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power *** multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision *** achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical *** entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best *** address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart *** model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational ***,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one *** not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual *** comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

关键词： Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch

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