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Data-Driven Learning and control with Event-Triggered Measurements

IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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Admittance control Based Gait Intervention Strategy of Knee Exoskeleton for Stance Phase Support

Admittance Control Based Gait Intervention Strategy of Knee ...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Xiaolin Dai Zhihao Zhou Zilu Wang Xuewen Rong Qining Wang School of Control Science and Engineering Shandong University Jinan China University of Health and Rehabilitation Sciences Qingdao China Institute for Artificial Intelligence Peking University Beijing China Beijing Engineering Research Center of Intelligent Rehabilitation Engineering Beijing China College of Engineering Peking University Beijing China School of Control Science and Engineering Shandong University and Engineering Research Center of Intelligent Un-manned System of Ministry of Education Jinan China Department of Advanced Manufacturing and Robotics College of Engineering with the Institute for Artificial Intelligence Peking University Beijing China Peking University Third Hospital Beijing China Fifth Affiliated Hospital of Zhengzhou University Henan China

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

Knee exoskeletons have been utilized for gait rehabilitation in patients after total knee arthroplasty. During the early and middle stance phases of walking gaits, the knee joint activity of these patients is either absent or reduced, which is not conducive to the rehabilitation of normal gaits. Currently, most exoskeletons used for the rehabilitation of patients after TKA correct gait by pre-setting the movement trajectory of the knee joint. However, this method faces challenges in terms of human-machine interaction and safety. This paper proposes a strategy that combines admittance control during the early and middle stance phases with position control during the late stance and swing phases to improve compliance and safety between the wearer and the knee exoskeleton. The exoskeleton can provide support to the knee joint during the early and middle stance phases according to the wearer’s actual movement requirements. The performance and safety of the admittance control were verified through test bench and squat-to-stand experiments, while the gait intervention strategy was validated through a downhill walking experiment.

关键词： Knee Legged locomotion Torque Mechatronics Exoskeletons Position control Safety

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Biosignal-Based Attention Monitoring for Evaluating Train Driver Safety-Relevant Tasks

SSRN

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SSRN 2024年

作者： Kim, Jung Hwan Kim, Yeongjun Cho, Younggeol Kim, Tae Kyun Jang, Tongil Park, Chanwoo Kang, Seongkeun Advanced Instrumentation & Control Research Section Korea Atomic Energy Research Institute Daejeon Korea Republic of Department of Nuclear & Quantum Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Railroad Electrical & Electronic Engineering Korea National University of Transportation Uiwang Korea Republic of System Safety Research Department Korea Railroad Research Institute Uiwang Korea Republic of

This study explores the impact of phone usage on train driver performance, a critical aspect influencing the safety of railroad operations. The persistent problem of phone distractions, which severely impair situational awareness and contribute to accidents, necessitates innovative solutions to enhance operational safety. Leveraging advancements in technology, this study introduces a novel approach combining electroencephalogram (EEG)-based analysis with deep learning algorithms to assess the effects of phone usage on the attention states of train drivers. Utilizing a full-type train simulator to replicate real-life scenarios, this study conducts an experimental design involving scenarios with and without phone engagement, aiming to provide a comprehensive assessment of phone usage on performance in critical situations. The deep learning model employed achieved an impressive classification accuracy of approximately 85.6% in distinguishing attention states among 25 participants, with high precision and recall values indicating its efficacy in detecting phone interaction. Moreover, findings reveal that phone usage leads to a 1.4 times increase in response time to situational awareness, significantly affecting driver performance. These findings highlight the significant impact of phone usage on train driver attention and underscore the potential of EEG-based analysis and deep learning in improving railroad safety measures. This study contributes to the growing body of safety literature by focusing on train drivers, offering fresh insights and practical implications for enhancing the safety and efficiency of railroad operations. © 2024, The Authors. All rights reserved.

关键词： Railroads

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Local to Global: A Distributed Quantum Approximate Optimization Algorithm for Pseudo-Boolean Optimization Problems

arXiv

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arXiv 2023年

作者： Yue, Bo Xue, Shibei Pan, Yu Jiang, Min Dong, Daoyi The Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Electronics and Information Engineering Soochow University Suzhou215006 China The School of Engineering Australian National University CanberraACT2601 Australia

With the rapid advancement of quantum computing, Quantum Approximate Optimization Algorithm (QAOA) is considered as a promising candidate to demonstrate quantum supremacy, which exponentially solves a class of Quadratic Unconstrained Binary Optimization (QUBO) problems. However, limited qubit availability and restricted coherence time challenge QAOA to solve large-scale pseudo-Boolean problems on currently available Near-term Intermediate Scale Quantum (NISQ) devices. In this paper, we propose a distributed QAOA which can solve a general pseudo-Boolean problem by converting it to a simplified Ising model. Different from existing distributed QAOAs’ assuming that local solutions are part of a global one, which is not often the case, we introduce community detection using Louvian algorithm to partition the graph where subgraphs are further compressed by community representation and merged into a higher level subgraph. Recursively and backwards, local solutions of lower level subgraphs are updated by heuristics from solutions of higher level subgraphs. Compared with existing methods, our algorithm incorporates global heuristics into local solutions such that our algorithm is proven to achieve a higher approximation ratio and outperforms across different graph configurations. Also, ablation studies validate the effectiveness of each component in our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Ising model

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High Sensitivity Submicron Scale Temperature Sensor Based on Perovskite Nanoplatelet Lasers

SSRN

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SSRN 2024年

作者： Zhao, Ruofan Tao, Jianxun Xi, Yuying Chen, Jiangzhao Ji, Ting Wang, Wenyan Wen, Rong Cui, Yanxia Chen, Junsheng Lia, Guohui College of Physics and Optoelectronics Key Laboratory of Interface Science and Engineering in Advanced Materials Key lab of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of Technology Taiyuan030024 China School of Materials Science and Engineering Kunming University of Science and Technology Kunming650093 China Shanxi-Zheda institute of advanced materials and chemical engineering China Nano-Science Center Department of Chemistry University of Copenhagen Universitetsparken 5 CopenhagenDK-2100 Denmark

Submicron scale temperature sensors are crucial for a range of applications, particularly in micro and nanoscale environments. One promising solution involves the use of active whispering gallery mode (WGM) microresonators. These resonators can be remotely excited and read out using free-space structures, simplifying the process of sensing. In this study, we present a submicron-scale temperature sensor with a remarkable sensitivity up to 190 pm/℃ based on a triangular CH3NH3PbI3 nanoplatelet (NPL) laser. Notably, as temperature changes, the peak wavelength of the laser line shifts linearly. This unique characteristic allows for precise temperature sensing by tracking the peak wavelength of the NPL laser. The optical modes are confined within the perovskite NPL, which measures just 85 nanometers in height, due to total internal reflection. Our NPL laser boasts several key features, including a high Q-factor of ~ 2610 and a low laser threshold of about 19.8 µJ cm−2. The combination of exceptional sensitivity and ultra-small size makes our WGM device an ideal candidate for integration into systems that demand compact temperature sensors. This advancement paves the way for significant progress in the development of ultrasmall temperature sensors, opening new possibilities across various fields. © 2024, The Authors. All rights reserved.

关键词： Perovskite

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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Historical Air Pollutant Emissions and Future Sustainable Pathways of Global Cement Plants

SSRN

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SSRN 2024年

作者： Yan, Xizhe Tong, Dan Cao, Zhi Chen, Cuihong Guo, Yaqin Xu, Ruochong Qin, Xinying Fu, Yujia Liu, Yang Zheng, Dongsheng He, Kebin Zhang, Qiang Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Tsinghua University Beijing100084 China College of Environmental Science and Engineering Nankai University Tianjin300350 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China

Assessing historical emission trajectories and forecasting future changes at the unit level for the global cement industry are vital for summarizing past experiences in green management and designing a plant-by-plant transition roadmap. Here, we evaluate the evolution of major air pollutant emissions during 1990−2020 and explore mitigation pathways for future sustainable development, based on a developed unit-level global cement emissions database. Our findings indicate that strengthening emission standards is an effective strategy for decoupling air pollutant emissions from clinker production, particularly for emerging economies. Without stringent emission controls, these economies are poised to become major contributors to pollutant emissions due to growing demand. To promote environmental sustainability, emerging economies should enhance emission controls to reverse the emission growth driven by demand expansion, while China and developed countries should deepen their emission mitigation efforts. Our results highlight the importance of upgrading end-of-pipe controls to ensure sustainability within the global cement industry. © 2024, The Authors. All rights reserved.

关键词： Sustainable development

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Quality Prediction of Injection Molding Process Using CNN and Parallel-Cascade LSTMs

Quality Prediction of Injection Molding Process Using CNN an...

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第43届中国控制会议

作者： Yibo Zhang Dewei Li Yuanqiang Zhou Furong Gao Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University College of Electronic and Information Engineering Tongji University Department of Chemical and Biological Engineering Hong Kong University of Science and Technology Clear Water Bay KowloonHong Kong and the Guangzhou HKUST Fok Ying Tung Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Quality prediction of plastic products has always been an important research topic in the injection molding production process. However, existing quality prediction methods struggle to handle both high-frequency data and statistical data generated during the injection molding production process. This paper introduces a novel quality prediction model for injection molding production, utilizing a combination of Convolutional Neural Networks(CNN) and Parallel-Cascade Long Short-Term Memory(PCLSTM) networks. The PCLSTM network is designed to handle the complexities of high-frequency data in injection molding,overcoming the limitations of traditional LSTM methods in processing long sequence data. By segmenting data into stages and utilizing a cascade mechanism, the PCLSTM architecture efficiently captures important temporal dynamics. Additionally, the CNN network is designed to extract statistical data features. The quality prediction results are obtained by fusing two types of data features and then passing them through a fully connected network. Experimental results on real production datasets show that the CNN-PCLSTM model outperforms existing models in prediction accuracy and training convergence speed.

关键词： LSTM CNN Quality Prediction Injection Molding Process

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Photovoltaic Utilization Benefit Evaluation of Flexible DC Traction Power systems

Photovoltaic Utilization Benefit Evaluation of Flexible DC T...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Zhanhe Li Kaibin Miao Weihan Bao Chao Lu Xiaoqian Li Lingyun Zhang Dept. of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China China Academy of Railway Science Co. Ltd Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

With rising demands for environmental protection and energy efficiency, integrating photovoltaic (PV) resources into flexible DC traction power systems (TPSs) has become increasingly important. However, PV integration faces two main challenges: the volatility of PV output and traction load uncertainties, as well as the effect of power flow optimization under coordinated control. Current methods, including dynamic simulation, power balance analysis, and power flow analysis, struggle to comprehensively evaluate these factors. To address this, this study proposes a PV Utilization Benefit (PVUB) evaluation model, which quantifies the operational benefits of PV integration by accounting for both PV and traction load uncertainties and power flow optimization effects. Applied to a real-world case on a Beijing Metro Line, the PVUB model effectively analyzes investment benefits and the impact of key factors, providing essential technical support for PV planning and power flow analysis of PV-integrated flexible DC TPSs.

关键词： Resistance Photovoltaic systems Uncertainty Evaluation models Traction power supplies Planning Indexes Optimization Load flow analysis Investment

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Reviewing Fault Diagnosis Methods in Electric Drives: Power Subsystem and Electrical Machine

Reviewing Fault Diagnosis Methods in Electric Drives: Power ...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Galina Demidova Hao Chen Yibin Li Dmitry Lukichev Alecksey Anuchin Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining and Technology Xuzhou China Institute of Marine Science and Technology Shandong University Shandong China Department of Electric Drives Moscow Power Engineering Institute Moscow Russia

This paper provides a review of popular fault diagnosis methods in the power subsystem and electrical machine of electric drives. The review aims to explore the different approaches used to detect faults in these components and the most common types of faults encountered in electric drives. The paper first provides an overview of the power subsystem and electrical machine and their respective functions within the electric drive system. It then proceeds to discuss the different types of faults that can occur in these components, including overvoltage, undervoltage, overcurrent, and short circuits. The review evaluates the various fault diagnosis methods available for these components, including model-based, signal-based, and artificial intelligence-based approaches. Model-based approaches use mathematical models of the system to detect and diagnose faults. Signal-based approaches rely on the analysis of measured signals to detect changes in system behavior that indicate a fault. Artificial intelligence-based approaches use machine learning algorithms to identify patterns in data and detect faults. Model-based approaches are highly accurate but can be computationally intensive. Signal-based approaches are less computationally intensive but may be less accurate in certain situations. Artificial intelligence-based approaches are versatile but require significant amounts of data for training. This review provides a comprehensive overview of the current state-of-the-art in fault diagnosis methods for the power subsystem and electrical machine of electric drives. The review concludes by suggesting potential areas for future research to further improve the accuracy and efficiency of fault diagnosis methods for electric drives.

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