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Data Driven control and Learning Systems (DDCLS)

作者： Penghao Wu Hongfeng Tao Engang Tian Tangwen Yin Hongtian Chen Yiyang Chen School of Mechanical and Electrical Engineering Soochow University Suzhou P. R. China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi P. R. China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai P. R. China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods.

关键词： Fault diagnosis Learning systems Employee welfare Sensitivity Power supplies Noise Electric vehicles

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Quantized Distributed Nonconvex Optimization with Linear Convergence

Quantized Distributed Nonconvex Optimization with Linear Con...

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IEEE Conference on Decision and control

作者： Lei Xu Xinlei Yi Jiayue Sun Yang Shi Karl Henrik Johansson Tao Yang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper considers distributed nonconvex op-timization for minimizing the average of local cost functions, by using local information exchange over undirected communication networks. Since the communication channels often have limited bandwidth or capacity, we first introduce a quantization rule and an encoder/decoder scheme to reduce the transmission bits. By integrating them with a distributed algorithm, we then propose a distributed quantized nonconvex optimization algorithm. Assuming the global cost function satisfies the Polyak– Łojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that the proposed algorithm linearly converges to a global optimal point. Moreover, a low data rate is shown to be sufficient to ensure linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical simulation examples.

关键词： Quantization (signal) Communication channels Bandwidth Cost function Numerical simulation Communication networks Distributed algorithms

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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Development of a Graph Model and Algorithm to Analyze the Dynamics of a Linear System with Delay

Development of a Graph Model and Algorithm to Analyze the Dy...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Shakhnoza R. Ubaydulayeva Aziz M. Nigmatov Department “Automation and control of technology process in production” Tashkent institute of irrigation and agricultural mechanization engineers

ISBN: (数字)9781728145907

ISBN: (纸本)9781728145914

The article deals with the problem of modeling and calculating processes in a linear system with a delay based on dynamic graphs. A graph model of a continuous lagging signal (initial function) is constructed. A graph model of a linear stationary system with a delay in the feedback chain is constructed as a union of graph models of its elements. The graph model makes it obvious that a system with a delay in the feedback circuit is open relative to the signals flowing in it, which makes it possible to significantly simplify computational procedures. A linear system with a delayed control signal is also considered. Based on graph models the authors obtain the recurrent relations and an algorithm for calculating processes in a linear system with a delay in state and a delay in control. The paper presents an example of using the obtained algorithm. The proposed method allows for a clear and strict formalization of the description and analysis of complex systems, including nonlinear discrete systems with different types of modulation.

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Resilient control of Networked control Systems With Hidden DoS Attacks and Unknown Observation Probability

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IEEE Transactions on control of Network Systems 2025年

作者： Wang, Yuan Yan, Huaicheng Park, Ju H. Zhang, Hao Shen, Hao East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China Suzhou University of Science and Technology School of Electronic and Information Engineering Suzhou215009 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education Shanghai200237 China Weifang University College of Machinery and Automation Weifang261061 China Yeungnam University Department of Electrical Engineering Gyeongsan38541 Korea Republic of Tongji University Department of Control Science and Engineering Shanghai200092 China Anhui University of Technology School of Electrical and Information Engineering Ma'anshan243002 China

This paper systematically studies the problem of resilient controller design for networked control systems under imperfect communication environments. The denial of service (DoS) attacks in the case of an imperfect network are remodeled. The semi-Markov chain is introduced in this paper to provide a suitable description of random attacks. On this basis, the double-layer stochastic process is constructed, which consists of an attack sequence and an observation one. Second, since the attack modes are inaccessible, a controller is constructed based on the observation sequence by virtue of the emission probability to stabilize the system under random attacks. Due to the complexity of the network, the probability that an observer can accurately detect an attack when it occurs is considered unknown. Based on the introduced double-layer stochastic process, a mode-associated Lyapunov function is constructed to facilitate the stability analysis. Building on this foundation, sufficient conditions that can ensure the δ-error mean square stability of the closed-loop system are derived. In the end, a simulation of the unmanned ground vehicle is presented, showcasing the feasibility and validity of the proposed control scheme. © 2025 IEEE.

关键词： Stochastic systems

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Submodularity-based sensor scheduling for state estimation among multiple linear dynamical systems

Submodularity-based sensor scheduling for state estimation a...

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Jiangsu Annual Conference on automation (JACA 2022)

作者： Y. Ni C. Yang X. Liu X. Luan F. Liu The Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things Engineering Jiangnan University Wuxi People's Republic of China The Key Laboratory of Smart Manufacturing in Energy Chemical Process (Ministry of Education) Department of Automation East China University of Science and Technology Shanghai People's Republic of China Department of Radiology Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital/Harvard Medical School Boston USA

In this paper we focus on the sensor scheduling for dynamic state estimation among multiple processes. Multiple sensors transmit their measurements to the remote estimator via a shared wireless channel. Only a subset of sensors are allowed to transmit at each time. The objective of the scheduler is to maximize a linear combination of the terminal time estimation error covariances in a log-determinant sense. We develop conditions under which the objective is monotone and submodular. A submodularity-based greedy scheduling algorithm is proposed, and its performance bound is provided. Numerical examples are presented to illustrate the theoretical findings.

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Identification of Virtual Analyzers of Products' Quality Produced at Raw and Slop Oil Mixture with off Spec Products Distillation Units 3

Identification of Virtual Analyzers of Products' Quality Pro...

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3rd International Conference on control in Technical Systems, CTS 2019

作者： Ulazevich, V.Yu. Zhukov, I.V. Saint Petersburg Electrotechnical University 'Leti' Department of Automation and Control Processes Department of Automated Process Control Systems LLC KINEF Saint Petersburg Russia Department of Automated Process Control Systems LLC 'Kinef' Kirishi Russia

ISBN: (纸本)9781728125107

The object of the study is crude oil distillation unit that operates in conditions of strong variability of feed composition. The unit's feed components are crude oil, oil from oil traps and off spec products, at that feed components are mixed in a wide ratio range. It is shown that for objects of this type, it is necessary, along with other input variables, to use feed characterizing parameters. Model of the temperature of final boiling point of 105-180 °C cut from ELOU-AT-1 (Electrical Desalting Unit) of LLC 'KINEF' has been initialized and tested. © 2019 IEEE.

关键词： Distillation

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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A safe control scheme for the dense medium coal separation process based on Bayesian network and Active Learning

A safe control scheme for the dense medium coal separation p...

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第33届中国控制与决策会议

作者： Wenchao Bao Fei Chu Chao Shang Tao Chen Fuli Wang Furong Gao China University of Mining and Technology School of Information and Control EngineeringUnderground Space Intelligent Control Engineering Research Center of the Ministry of Education Department of Automation Tsinghua University Department ofChemistry and Process Engineering University of Surrey School of Information Science and Engineering Northeastern University Department of Chemical Engineering Hong Kong University of Science and Technology

The paper presents a safe control method for the operation of dense medium coal preparation process based on Bayesian network(BN) and active *** dense medium coal preparation process fluctuates significantly in operating conditions and abnormal operating conditions occur frequently due to the uncertainty in the *** address this issue,we establish the BN model of safety control by integrating qualitative expert knowledge and quantitative data information based on the analysis of causes of abnormal conditions and corresponding operation schemes,which acts as the basis for the safety *** the previous BN structure learning method based on observation data information can only get Markov equivalence class and needs a large amount of data,it is difficult to establish an accurate *** address this issue,we propose a BN structure learning method based on active learning to improve its efficiency and *** results show that the proposed method can effectively provide safety control decisions for abnormal conditions in the dense medium coal preparation process.

关键词： Safe control Bayesian network Active learning Dense medium coal separation

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Research of the Influence of the Training Sample Frequency on the Quality of Virtual Analyzers of Oil Refining Products

Research of the Influence of the Training Sample Frequency o...

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IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

作者： Vladislav Yu.Ulazevich Department of Automation and Control Processes Department of Automated Process Control Systems Saint Petersburg Electrotechnical University 'LETI' LLC «KINEF» St. Petersburg Russia

ISBN: (数字)9781728157610

ISBN: (纸本)9781728157627

Training sample rate selection is one of the controversial issues arising in the process of virtual sensor development. Increasing the sample rate results in the initialization execution time extension, but it can also have a positive effect on the quality of the resulting models. In the study the virtual analyzer of the final boiling point for 62-105 °C fraction was initialized in five time segments with different sample rate using industrial data obtained from ELOU-AVT-2 unit of Kirishi refinery "KINEF" and EM-algorithm.

关键词： Training Analytical models Delays Oils Mathematical model Refining Data models

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