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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Digital Futures Stockholm10044 Sweden The Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve O(T max{κ,1-κ}) static network regret and O(T 1-κ/2) network cumulative constraint violation, where T is the time horizon and κ ∈ (0, 1) is a user-defined trade-off parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to O(T κ). Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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Determination the installation efficiency of the evaporative air cooling in the greenhouse by temperature-moisture regime 1

Determination the installation efficiency of the evaporative...

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1st International Conference on Energetics, Civil and Agricultural engineering, ICECAE 2020

作者： Anarbaev, A. Tursunov, O. Zakhidov, R. Kodirov, D. Vakhidov, U. Bozorov, E. Tuhtaeva, G. Babaev, A. Department of Power Supply and Renewable Energy Sources Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent100000 Uzbekistan School of Mechanical and Power Engineering Shanghai Jiao Tong University Shanghai200240 China Research Institute of Forestry Tashkent111104 Uzbekistan Department of Automation and Control of Technology Process Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Tashkent100000 Uzbekistan Department of Hydrology and Ecology Bukhara Branch of Tashkent Institute of Irrigation and Agricultural Mechanization Engineers Bukhara Uzbekistan

The results of theoretical studies and experiments of the developed exhaust ventilation system with direct evaporative air-cooling with using "wet mats"are given. It has shown sufficient efficiency with increasing quality of flower cutting in the summer months. The effect of moisture on the operation of this system was studied. It is established that the heat absorption capacity of air directly depends on its temperature and relative humidity. It is also determined that the temperature of leaves and flowers in summer is usually 2 ÷ 7 °C greater than the air temperature in the greenhouses. In an experimental greenhouse, according to the results of experiments, the effectiveness of the developed installation for evaporative cooling of its operation was determined. © Published under licence by IOP Publishing Ltd.

关键词： Greenhouses

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An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

SSRN

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SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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Quantification of C02 emission reductions from energy efficiency actions and solar photovoltaic generation at the Federal institute of Education, Science, and Technology of Goiás

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2021年 19卷 45-50页

作者： Vidal, M.A. Faria, A.F. Stach, A.H.M. Netou, D.P. Pinto, L.S. Pinlio, A.G. Domingues, E.G. Master’s Program in Technology of Sustainable Process Federal Institute of Education Science and Technology Goias-Cainpuj Rua 75. n°46 - Centro Goiania - GO74055-110 Brazil Electiical Engineering Control and Automation Engineering Program Federal Institute of Education Science and Technology Goias-Cainpuj Rua 75. n°46 - Centro Goiania - GO74055-110 Brazil NeXT - Nucleus of Experimental and Technological Studies Federal Institute of Education Science and Technology Goias-Cainpuj Rua 75. n°46 - Centro Goiania - GO74055-110 Brazil

In 2016. die Brazilian Agency of Electric Energy-launched a public call for research and development projects to reduce barriers to implementing energy efficiency projects and own electricity generation. One objective of this public call was to encourage pilot projects at the Brazilian public higher education institutions. In this context, the Federal institute of Education. Science, and Technology of Goias has approved the research project, entided "Energy efficiency and mini generation ai the Federal institute of Education. Science, and Technology - IFG." The project consists of implementing energy efficiency actions and installing photovoltaic distributed generation (mini generation) in twelve of the fourteen IFG campuses. The main objective of this work is to quantify the C0;emission reductions from die project through the methodologies used by the United Nations Framework Climate Change Convention for projects under the Clean Development Mechanism The emission reductions over the project lifetime was estimated at approximately 37,400 iC’O- equivalent, evidencing the environmental benefit to be provided by the referred project. © 2021, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Energy efficiency

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Preface

Advances in Industrial Control

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Advances in Industrial control 2023年 Part F1191卷 vii页

作者： Li, Jinna Lewis, Frank L. Fan, Jialu School of Information and Control Engineering Liaoning Petrochemical University Wanghua District Liaoning Fushun China UTA Research Institute University of Texas at Arlington Fort WorthTX United States State Key Lab of Synthetical Automation for Process Industries Northeastern University Heping District Liaoning Shenyang China

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Automated machine learning-assisted enhanced product carbon footprint tracking and analysis in refinery industry: A graph-based life cycle assessment framework

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Journal of Cleaner Production 2025年 514卷

作者： Zhang, Tingwei Peng, Xin Zhong, Weimin Shen, Feifei Ding, Jinliang State Key Laboratory of Industrial Control Technology East China University of Science and Technology Shanghai200237 China State Key Laboratory of Chemical Engineering East China University of Science and Technology Shanghai200237 China School of Information Science and Engineering East China University of Science and Technology Shanghai200237 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Huzhou Institute of Industrial Control Technology Huzhou313099 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning Shenyang110819 China

Product carbon footprint assessment and tracking in the refinery industry mainly rely on the expertise of carbon management professionals, which leads to high service costs and involves significant manual intervention, reducing the accuracy of carbon footprint analysis. Accordingly, a data-driven automated product carbon footprint analysis framework based on life cycle assessment is developed in this work. Specifically, an integrated material-energy-carbon multi-scale emission accounting model is established to quantify CO2 emissions accurately. Subsequently, considering that traditional carbon emission allocation methods are generally based on fixed assumptions or simplified proportional relationships, which fail to fully account for the dynamic variations in complex production processes, the allocation results lack accuracy and reliability. Additionally, these methods lack sufficient flexibility and are unable to effectively adjust to the actual carbon emission characteristics of different production stages and products. A data-driven contribution-based carbon emission allocation method is proposed from the carbon flow supply–demand balance perspective to improve allocation transparency. Moreover, a graph-based automated recognition framework of product carbon footprint chains is developed to achieve process operation automation and eliminate manual operations. The proposed models and methods are applied to a real refinery production site for a case study. The results show that the developed product carbon footprint analysis framework can highlight high-energy consumption and high-emission links in the production process and pay more attention to the environmental costs of high-value products. Moreover, CO2 can act as a carbon flow regulation hub, effectively managing emissions through the balance of carbon flow supply and demand. Light oil has a higher contribution rate to CO2 emissions than heavy oil. The carbon footprint of petroleum coke is 3.8677 tCO2eq/t, ma

关键词： Carbon footprint

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Time-delay assisted mechanism and adaptive LSTM hybrid train braking model of heavy haul trains

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control engineering Practice 2025年 163卷

作者： Qiang Liu Kexuan Xu Yating Fu Jiang Liu Ling Liu State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University) Shenyang 110819 China Key Laboratory of Advanced Control and Optimization of Jiangxi Province Nanchang 330013 China School of Electrical and Automation Engineering East China Jiaotong University Nanchang 330013 China Frontiers Science Center for Smart High-speed Railway System Beijing 100044 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 China Beijing National Railway Research & Design Institute of Signal Communication Group Company Beijing 100070 China

The train braking model (TBM) that describes the dynamic relations of operation speed, mileage, and control force is essential for achieving stable operation and precise stopping of heavy haul trains (HHTs). However, it is difficult to establish the TBM of HHTs due to complex characteristics: (i) the long body and air braking process of the HHTs may lead to unexpected time-delays of control force; and (ii) there are significant unmodeled dynamics caused by rough tracks and external poor environment. Traditional TBM does not take into account the unmodeled dynamics and time-delays caused by air transmission during braking. To address these issues, this study proposes a data mechanism hybrid modeling strategy, which incorporates a braking time-delay assisted mechanism model and an adaptive long and short-term memory (LSTM) model. A new Bayesian optimization based time-delay estimation method is first proposed to determine unknown time-delays of each carriage and the estimated time-delays are incorporated to generate the multi-point-mass kinetic mechanism model. Moreover, the error of the mechanism-driven model is adaptively compensated by a sliding window LSTM model to conduct the unmodeled dynamics. The effectiveness of the proposed method is demonstrated using the field data.

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Graphical Minimax Game and On-Policy Reinforcement Learning for Consensus of Leaderless Multi-Agent Systems *

Graphical Minimax Game and On-Policy Reinforcement Learning ...

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International Conference on control and automation (ICCA)

作者： Wei Dong Chunyan Wang Jinna Li Jianan Wang Key Laboratory of Dynamics and Control of Flight Vehicle within Ministry of Education School of Aerospace Engineering Beijing Institute of Technology Beijing China School of Information and Control Engineering Liaoning Shihua University Fushun China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

In this paper, we study the adaptive optimal consensus control of leaderless multi-agent systems (MASs) with heterogeneous dynamics. First, the consensus control problem is converted into a graphical minimax game problem and the corresponding algebraic Riccati equation (ARE) for each agent is obtained. Then, an on-policy reinforcement learning algorithm is proposed to online learn the optimal control policy without requiring the system dynamics. A certain rank condition is established to guarantee the convergence of the proposed online learning algorithm to the unique solution of the ARE. Finally, the effectiveness of the proposed algorithm is demonstrated through a numerical simulation.

关键词： Games Heuristic algorithms Reinforcement learning Mathematical model Vehicle dynamics Performance analysis Optimal control

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Fault Detection and Correction in Omni Bundle Robot Using EKF

Fault Detection and Correction in Omni Bundle Robot Using EK...

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International Conference on Manufacturing, Advanced Computing, Renewable Energy and Communication, MARC 2018

作者： Rana, Rohit Agarwal, Vijyant Gaur, Prerna Parthasarathy, Harish Instrumentation and Control Engineering Division Netaji Subhas Institute of Technology Sec-03 Dwarka110078 India Manufacturing Process and Automation Engineering Division Netaji Subhas Institute of Technology Sec-03 Dwarka110078 India Electronics and Communication Engineering Division Netaji Subhas Institute of Technology Sec-03 Dwarka110078 India

ISBN: (纸本)9789811367717

In this paper, the entire focus is on fault detection and correction using extended Kalman filter (EKF) on industrially used omni bundle robot. Here, the authors considered a problem where a robot does monotonous job and repetitively traverse the same trajectory every time. Due to wear and tear or any fluctuation in input signals, the robot may not track the desired trajectory. Faults may arise at various levels in robots, for example, in actuator, encoder, and dynamical parameters. Actuator fault detection using encoder sensor will be the main focus in this research paper. The dynamics of robotic arm link is modeled using a DC motor attached with the link, and the encoder attached with the motor will provide position feedback. The fault is detected using EKF state estimation, and the fault is corrected using a PID controller feedback into the motor input torque. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Extended Kalman filters

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Synthetic series of electricity generation through a photovoltaic system by using different panel temperature models

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2021年 19卷 39-44页

作者： Stach, A.H.M. Neto, D.P. Vidal, M.A. Souto, O.C.N. Silva, S.B. Domingues, E.G. Master’s Program in Sustainable Process Technology Federal Institute of Education Science and Technology of Goiás Brazil Rua 75 nº46-Centro Goiânia-GO74055-110 Brazil Electrical Engineering/Control and Automation Engineering Program Federal Institute of Education Science and Technology of Goiás Brazil Rua 75 nº46-Centro Goiânia-GO74055-110 Brazil NeXT-Nucleus of Experimental and Technological Studies Federal Institute of Education Science and Technology of Goiás Brazil Rua 75 nº46-Centro Goiânia-GO74055-110 Brazil

The electric energy produced by photovoltaic systems is a function of several variables that present random behavior, such as solar irradiance, ambient temperature, wind speed, and relative humidity. Several models describe the energy produced by these systems, which reflect the various factors that influence the generation and how significant these influences are. In this way, the present work aims to compare some panel temperature models and the electrical energy produced from a photovoltaic system located in the Midwest of Brazil. The applied methodology aims to evaluate the expected averages and standard deviations of the monthly synthetic series of generated energy to identify the main random variables that influence the electric generation. The results indicate the model that best describes the physical system concerning the randomness of the variables involved, the photovoltaic technology, the location, and the measurement period. © 2021, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Stochastic models

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