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arXiv 2021年

作者： Liu, Yuhan Tóth, Roland The Harbin Institute of Technology Department of Control Science and Engineering Harbin China The Eindhoven University of Tech. Dep. of Electrical Eng. Control Systems Group Eindhoven Netherlands The Institute for Computer Science and Control Budapest Hungary

An important issue in quadcopter control is that an accurate dynamic model of the system is nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation is an effective tool to learn unknown dynamics from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control strategy that improves the performance of a quadcopter during trajectory tracking. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation in an efficient way. Numerical simulations are used to demonstrate the effectiveness of the proposed strategy. © 2021, CC BY-NC-SA.

关键词： Model predictive control

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Toward parallel intelligence: an interdisciplinary solution for complex systems

arXiv

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arXiv 2023年

作者： Zhao, Yong Zhu, Zhengqiu Chen, Bin Qiu, Sihang Huang, Jincai Lu, Xin Yang, Weiyi Ai, Chuan Huang, Kuihua He, Cheng Jin, Yucheng Liu, Zhong Wang, Fei-Yue College of Systems Engineering National University of Defense Technology Changsha410073 China Hunan Institute of Advanced Technology Changsha410073 China Shanghai Institute of Infectious Disease and Biosecurity Shanghai200032 China Neuherberg Germany Department of Computer Science Hong Kong Baptist University 999077 Hong Kong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in Artificial systems, Computational experiments, and Parallel execution (ACP) approach has been developed. The method cultivates a cycle, termed parallel intelligence, which iteratively creates data, acquires knowledge, and refines the actual system. Over the past two decades, the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines, offering versatile interdisciplinary solutions for complex systems across diverse fields. This review explores the origins and fundamental concepts of the parallel systems method, showcasing its accomplishments as a diverse array of parallel technologies and applications, while also prognosticating potential challenges. We posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation. © 2023, CC BY-NC-ND.

关键词： Iterative methods

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Optimal convergence rates of totally asynchronous optimization

arXiv

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arXiv 2022年

作者： Wu, Xuyang Magnńsson, Sindri Feyzmahdavian, Hamid Reza Johansson, Mikael The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Department of Computer and System Science Stockholm University StockholmSE-164 07 Sweden ABB Cooperate Research VästeråsSE-721 78 Sweden

Asynchronous optimization algorithms are at the core of modern machine learning and resource allocation systems. However, most convergence results consider bounded information delays and several important algorithms lack guarantees when they operate under total asynchrony. In this paper, we derive explicit convergence rates for the proximal incremental aggregated gradient (PIAG) and the asynchronous block-coordinate descent (Async-BCD) methods under a specific model of total asynchrony, and show that the derived rates are order-optimal. The convergence bounds provide an insightful understanding of how the growth rate of the delays deteriorates the convergence times of the algorithms. Our theoretical findings are demonstrated by a numerical example. © 2022, CC BY.

关键词： Machine learning

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Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

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Qualitative Behavior of a Nonlinear Generalized Recursive Sequence with Delay

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Mathematical Problems in engineering 2021年第1期2021卷

作者： Ibrahim, Tarek F. Khan, Abdul Qadeer Ibrahim, Abdelhameed Department of Mathematics Faculty of Science Mansoura University Mansoura Egypt King Khalid University Abha Saudi Arabia Department of Mathematics University of Azad Jammu and Kashmir Muzaffarabad13100 Pakistan Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Difference equations are of growing importance in engineering in view of their applications in discrete time-systems used in association with microprocessors. We will check out the global stability and boundedness for... 详细信息

关键词： Difference equations

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Data-Driven Estimation of Infinitesimal Generators of Stochastic systems ⁎

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IFAC-PapersOnLine 2021年第5期54卷 277-282页

作者： Ameneh Nejati Abolfazl Lavaei Sadegh Soudjani Majid Zamani Department of Electrical Engineering Technical University of Munich Germany Institute for Dynamic Systems and Control ETH Zurich Switzerland School of Computing Newcastle University United Kingdom Department of Computer Science University of Colorado Boulder USA and LMU Munich Germany

This paper is concerned with a data-driven approach for the estimation of infinitesimal generators of continuous-time stochastic systems with unknown dynamics. We first approximate the infinitesimal generator of the solution process via a set of data collected from trajectories of the unknown system. The approximation utilizes both time discretization and sampling from the solution process. Assuming proper continuity assumptions on dynamics of the system, we then quantify the closeness between the infinitesimal generator and its approximation while providing a priori guaranteed confidence bound. We demonstrate that both the time discretization and the number of data play significant roles in providing a reasonable closeness precision. Moreover, for a fixed size of data, variance of the estimation converges to infinity when the time discretization parameter goes to zero. The formulated error bound shows how to pick proper data size and time discretization jointly to prevent this counter-intuitive behavior. The proposed results are demonstrated on a case study.

关键词： Continuous-time stochastic systems Data-driven estimation Infinitesimal generators

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Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison

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Scientific Reports 2025年第1期15卷 1-9页

作者： Altalbawy, Farag M. A. Alkhayyat, Ahmad Jhala, Ramdevsinh Yadav, Anupam Ramachandran, T. Shankhyan, Aman Karthikeyan, A. Thatoi, Dhirendra Nath Sinitsin, Vladimir Vladimirovich Department of Chemistry University College of Duba University of Tabuk Tabuk Saudi Arabia Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq Marwadi University Research Center Department of Mechanical Engineering Faculty of Engineering & Technology Marwadi University Gujarat Rajkot 360003 India Department of Computer Engineering and Application GLA University Mathura 281406 India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Karnataka Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Mechanical Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Mechanical Engineering Siksha ’O’ Anusandhan (Deemed to be University) Odisha Bhubaneswar 751030 India Research Laboratory of Technical Self-Diagnostics and Self-Control of Devices and Systems Department of Scientific and Innovative Activities Research Institute of Mechanical Engineering South Ural State University Lenin prospect 76 Chelyabinsk 454080 Russian Federation

This paper introduces an approach to temperature prediction by employing three distinct machine learning models: K-Nearest Neighbors (KNN), Gaussian Process Regression (GPR), and Multi-layer Perceptron (MLP) which are integrated into Computational Fluid Dynamics (CFD). The dataset consists of inputs, represented by the features x and y, and the corresponding output, which is the temperature. The case study is fluid flow of nanofluid through a pipe and the nanofluid contains CuO particles. To enhance model efficacy, the Political Optimizer (PO) algorithm was utilized for fine-tuning purposes. The findings substantiate the capability of the optimized frameworks in delivering precise temperature estimations. GPR yielded a notable R² value of 0.998, reflecting a strong concordance between the estimated and actual measurements. In a comparable manner, the KNN approach exhibited outstanding predictive performance, also attaining an R² value of 0.998. MLP, although slightly lower in performance compared to the other models, still proves to be a reliable predictor of temperature values, with an R-squared score of 0.984. Overall, the combination of the PO algorithm and the machine learning models showcases promising results in temperature prediction. The study’s findings offer valuable insights for decision-making processes in a variety of domains that rely on accurate temperature forecasting. © The Author(s) 2025.

关键词： Gaussian process regression Heat transfer K-Nearest neighbors Multi-layer perceptron Nanofluid

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Fixed-Time Virtual Inertia control for Frequency Stability in Islanded Mgs

Fixed-Time Virtual Inertia Control for Frequency Stability i...

引用

IEEE Industry Applications Society Annual Meeting (IAS)

作者： Mohamed Zaery Hossam Ali Syed Muhammad Amrr S.M. Suhail Hussain Vijay Vittal Mohammad A. Abido K.A.CARE Energy Research & Innovation Center King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Department of Electrical Engineering Faculty of Energy Engineering Aswan University Aswan Egypt School of Electrical Arizona State University Computer and Energy Engineering Tempe AZ USA Control & Instrumentation Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

This paper introduces a novel control strategy, Fixed-time Virtual Inertia (Fxt-VI), designed to enhance frequency stability in islanded microgrids (MGs) that typically lack synchronous generation. Unlike traditional power systems, which rely on physical inertia, MGs necessitate inertia emulation due to the integration of fluctuating renewable energy sources. A key feature of the proposed Fxt-VI control strategy is its ability to quickly resolve frequency deviations within a predefined time frame, regardless of the initial system conditions. This ensures accelerated and predictable stabilization times. A thorough analysis using Lyapunov-based method is developed to validate the stability of the designed control strategy within a fixed time. Simulations in an islanded microgrid setup, including various energy sources such as thermal, wind, solar power plants, and energy storage systems, demonstrate the efficacy of the proposed Fxt-VI controller. Moreover, this paper provides a comparative evaluation of the proposed FxT-VI controller in relation to robust $\mathrm{H}-\infty$ and optimal PI controllers. The results show that it not only achieves quicker stabilization but also maintains lower frequency deviations compared to traditional methods, as assessed through specific performance metrics.

关键词： Time-frequency analysis Renewable energy sources Simulation Microgrids Power system stability Stability analysis Time factors Thermal stability Frequency control Power generation

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Two-layer nonlinear control of DC-DC buck converters with meshed network topology

arXiv

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arXiv 2021年

作者： Baldivieso-Monasterios, Pablo R. Sadabadi, Mahdieh Konstantopoulos, George C. Department of Automatic Control & Systems Engineering University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom Department of Electrical and Computer Engineering University of Patras Patras26500 Greece

In this paper, we analyse the behaviour of a buck converter network that contains arbitrary, up to mild regularity assumptions, loads. Our analysis of the network begins with the study of the current dynamics;we propose a novel Lyapunov function for the current in closed-loop with a bounded integrator. We leverage on these results to analyse the interaction properties between voltages and bounded currents as well as between node voltages and to propose a two-layer optimal controller that keeps network voltages within a compact neighbourhood of the nominal operational voltage. We analyse the stability of the closed loop system in two ways: one considering the interconnection properties which yields a weaker ISS type property and a second that contemplates the network in closed loop with a distributed optimal controller. For the latter, we propose a novel distributed way of controlling a Laplacian network using neighbouring information which results in asymptotic stability. We demonstrate our results in a meshed topology network containing 6 power converters, each converter feeding an individual constant power load with values chaning arbitrarily within a pre-specified range. © 2021, CC BY-SA.

关键词： Asymptotic stability

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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