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Sliding Mode control in Power Converters and Drives: A Review

IEEE/CAA Journal of automatica Sinica 2022年第3期9卷 392-406页

作者： Ligang Wu Jianxing Liu Sergio Vazquez Sudip K.Mazumder IEEE Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Electronic Engineering Department Universidad de Sevilla41092 SevillaSpain Electrical and Computer Engineering Department University of Illinois ChicagoChicagoIL 60607 USA

Sliding mode control(SMC)has been studied since the 1950s and widely used in practical applications due to its insensitivity to matched *** aim of this paper is to present a review of SMC describing the key developments and examining the new trends and challenges for its application to power electronic *** fundamental theory of SMC is briefly reviewed and the key technical problems associated with the implementation of SMC to power converters and drives,such chattering phenomenon and variable switching frequency,are discussed and *** recent developments in SMC systems,future challenges and perspectives of SMC for power converters are discussed.

关键词： Motor drive power electronics systems sliding mode control

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A Generalized Current Balancing control for Series-Capacitor Buck Converter with Interleaved Phase Angle 24

A Generalized Current Balancing Control for Series-Capacitor...

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24th IEEE Workshop on control and Modeling for Power Electronics, COMPEL 2023

作者： Li, Chung-Yi Nien, Chin-Fu Lin, Li Chen, Hung-Chi Chang Gung University Department of Electronics Engineering Taiwan Chang Gung University Department of Computer Science and Information Engineering Taiwan National Yang Ming Chiao Tung University Institution of Electrical Control Engineering Department of Electrical and Computer Engineering Taiwan

ISBN: (纸本)9798350316186

In addition to increasing the output current, an interleaved buck converter can significantly reduce the current ripple at the output. However, the bottleneck of the interleaved buck converter application is the unbalanced inductor current. In order to achieve the current balance in the entire section of the duty cycle, this paper proposes a control architecture with interleaved alignment, using the adjustment of the duty cycle and phase offset, to analyze and realize a high output current and current balanced in three-arm series capacitor interleaved buck converters (SCIBCs) in this work. By analyzing and modeling the SCIBC and using a single-loop control architecture that feedbacks the output voltage, the proposed current-balancing method balances all inductors' current within the full duty cycle without any current sensor. In addition, the small-signal transfer function of the control signal to the output voltage and design of the controller parameters are derived according to the specification, and the SCIBC with the proposed current-balancing control with PI controller of the alignment of interleaved phase angle is constructed. The high output current and current-balancing characteristics of the proposed circuit control architecture are validated in the simulation tool SIMPLIS. © 2023 IEEE.

关键词： controllers

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced computer science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Receding-Horizon control of Constrained Switched Systems with Neural Networks as Parametric Function Approximators

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SN computer science 2023年第1期4卷 62页

作者： Markolf, Lukas Stursberg, Olaf Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Wilhelmshöher Allee 71-73 Kassel 34121 Germany

This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach. © 2022, The Author(s).

关键词： Model approximation Neural networks Online control Optimization Reinforcement learning Stability

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Security Issues in Software-Defined Networking (SDN) Environments 23

Security Issues in Software-Defined Networking (SDN) Environ...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Yalda, Khirota Gorgees Hamad, Diyar Jamal Tapus, Nicolae Okumus, Ibrahim Taner Politehnica University of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Erbil Polytechnic University Kurdistan Region IT Department Soran Technical College Erbil Iraq Kahramanmaras Sutcu Imam University Kahramanmaras Computer Engineering Faculty of Engineering Turkey

ISBN: (纸本)9798331540388

Software-Defined Networking (SDN) represents a significant shift in network architecture, providing exceptional programmability, flexibility, and simplified management. However, this paradigm shift introduces a unique set of security challenges that must be addressed to fully realize the potential of SDN. This paper examines the security issues in SDN environments, detailing the threats and vulnerabilities at various layers of the SDN architecture, including the control plane, data plane, and application plane. Through an extensive review of current literature, critical security challenges such as controller attacks, data plane breaches, and vulnerabilities in inter-plane communications are identified. Existing security solutions and mitigation strategies, such as authentication and authorization mechanisms, encryption techniques, and intrusion detection systems, are also explored. Furthermore, the paper discusses recent advances and emerging trends in SDN security, offering insights into ongoing research and future directions. The findings underscore the importance of robust security measures in ensuring the reliability and integrity of SDN deployments, providing a foundation for future innovation and development in this dynamic field. © 2024 IEEE.

关键词： Network intrusion

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A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

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Digital Communications and Networks 2024年第1期10卷 126-134页

作者： Naiyu Wang Wenti Yang Xiaodong Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and Computer Engineering North China Electric Power UniversityChina Department of Mathematics&Computer Science Fayetteville State UniversityFayettevilleNCUSA Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ07030USA Department of Computer Science and Engineering Qatar UniversityDohaQatar

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic ***,concerns have been raised over the security and privacy of the tons of traffic and vehicle *** this regard,Federated Learning(FL)with privacy protection features is considered a highly promising ***,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global *** existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same *** this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of *** improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving ***,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice *** security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

关键词： Federated learning Blockchain Privacy-preservation Homomorphic encryption Internetof vehicles

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Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling Under Data Governance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 351-361页

作者： Qin, Zhaoming Dong, Nanqing Liu, Di Wang, Zhefan Cao, Junwei EPFL Automatic Control Laboratory Lausanne1015 Switzerland Shanghai Artificial Intelligence Laboratory Shanghai200232 China Tsinghua University Department of Electrical Engineering Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

As a data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems. However, centralized training, the most common paradigm for MARL, limits large-scale deployment in communication-constrained cloud-edge environments. As a remedy, distributed training shows unparalleled advantages in real-world applications but still faces challenge with system scalability, e.g., the high cost of communication overhead during coordinating individual agents, and needs to comply with data governance in terms of privacy. In this work, we propose a novel MARL solution to address these two practical issues. Our proposed approach is based on actor-critic methods, where the global critic is a learned function of individual critics computed solely based on local observations of households. This scheme preserves household privacy completely and significantly reduces communication cost. Simulation experiments demonstrate that the proposed framework achieves comparable performance to the state-of-the-art actor-critic framework without data governance and communication constraints. © 2023 IEEE.

关键词： Reinforcement learning

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Probabilistic Tube-based control Synthesis of Stochastic Multi-Agent Systems under Signal Temporal Logic 63

Probabilistic Tube-based Control Synthesis of Stochastic Mul...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Vlahakis, Eleftherios E. Lindemann, Lars Sopasakis, Pantelis Dimarogonas, Dimos V. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden University of Southern California Viterbi School of Engineering Department of Computer Science Los AngelesCA90089 United States Queen's University School of Electronics Electrical Engineering and Computer Science Northern Ireland Belfast United Kingdom

ISBN: (纸本)9798350316339

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and relax the underlying stochastic problem with a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical. © 2024 IEEE.

关键词： Stochastic systems

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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Mrozek, Dariusz National Taiwan University Department of Computer Science and Information Engineering Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Norway Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Poland

ISBN: (纸本)9781665480451

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems. © 2022 IEEE.

关键词： Energy management

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A Distributed Optimization Algorithm Considering Inequality Constraints and its Application in Economic Dispatch Problem 7

A Distributed Optimization Algorithm Considering Inequality ...

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7th International Conference on Power and Energy Applications, ICPEA 2024

作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, Witold Sun, Changhai School of Control Science and Engineering Dalian University of Technology Liaoning Dalian116023 China University of Alberta Department of Electrical and Computer Engineering Edmonton Canada School of Electrical Engineering Dalian University of Technology Liaoning Dalian116023 China

ISBN: (纸本)9798350356113

The economic dispatch problem (EDP) is crucial in optimizing and controlling power systems. As modern power system become more complex, traditional centralized communication methods are becoming less reliable. Therefore, distributed optimization method that relie on communication between neighboring nodes without the need for a central node are proposed. Existing distributed optimization methods for economic dispatch problems mainly focus on equality constraints like supply-demand balance, while problems with global inequality constraints are rarely studied. To address this gap, we propose a distributed optimization algorithm based on the primal-dual method and gradient tracking technique that can handle both global equality and inequality constraints. We thoroughly analyze its convergence and successfully apply it to the EDP with emission constraints to demonstrate its effectiveness. © 2024 IEEE.

关键词： Optimization algorithms

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