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arXiv 2022年

作者： Verhoek, Chris Beintema, Gerben I. Haesaert, Sofie Schoukens, Maarten Tóth, Roland The Control Systems Group Eindhoven University of Technology Netherlands The Institute for Computer Science and Control Budapest Hungary

The Linear Parameter-Varying (LPV) framework provides a modeling and control design toolchain to address nonlinear (NL) system behavior via linear surrogate models. Despite major research effort on LPV data-driven modeling, a key shortcoming of the current identification theory is that often the scheduling variable is assumed to be a given measured signal in the data set. In case of identifying an LPV model of a NL system, the selection of the scheduling map, which describes the relation to the measurable scheduling signal, is put on the users' shoulder, with only limited supporting tools available. This choice however greatly affects the usability and complexity of the resulting LPV model. This paper presents a deep-learning-based approach to provide joint estimation of a scheduling map and an LPV state-space model of a NL system from input-output data, and has consistency guarantees under general innovation-type noise conditions. Its efficiency is demonstrated on a realistic identification problem. © 2022, CC BY-NC-SA.

关键词： Nonlinear systems

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Design of controls for Boundedness of Trajectories of Multistable State Periodic systems

Design of Controls for Boundedness of Trajectories of Multis...

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IEEE Conference on Decision and control

作者： Jesus Mendoza-Avila Denis Efimov Ángel Mercado-Uribe Johannes Schiffer CNRS UMR 9189 - CRIStAL Inria Univ. Lille Lille France Control Systems and Network Control Technology Group Brandenburg University of Technology Cottbus Brandenburgische Technische Universität Cottbus-Senftenberg Cottbus Germany Fraunhofer Research Institution for Energy Infrastructures and Geothermal Systems (IEG) Cottbus Germany

In this paper, the concept of control Leonov functions is introduced, and it is shown that their information is enough to design continuous and periodic controllers that provide boundedness of the state for a class of multistable state-periodic systems. These feedback control laws are based on a mild adaptation of Sontag's universal formula, and a kind of small control property. The proposed method is illustrated via application in a microgrid.

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HIDG-based PSO Algorithm: Suspicious Sensor Selection for Power Grid Monitoring System Under Data Integrity Attack 5

HIDG-based PSO Algorithm: Suspicious Sensor Selection for Po...

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5th International Conference on Electrical Engineering and control Technologies, CEECT 2023

作者： Li, Pandeng Liang, Zhihong Yang, Yiwei Dong, Liangyu Suo, Yuhan Hao, Yi Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China Beijing Institute of Technology School of Automation Beijing China

ISBN: (纸本)9798350342253

With the development of communication and sensing technology, it has become possible to monitor the operating status of the power grid system through a series of sensors. However, malicious adversaries may launch data integrity attacks to compromise the measurements of certain sensors, causing the grid monitoring system to fail to grasp the correct system operating status. To solve the NP-hard suspicious sensor selection problem, this paper proposes an efficient attack detection scheduling algorithm, the Particle Swarm Optimization algorithm based on historical information directional guidance (HIDG-based PSO algorithm). The proposed algorithm is utilized with its unique evolutionary mechanism, which reduces the computational power requirements for sensor selection. For the problem of uncertainty in evolutionary algorithms, this paper uses historical information to guide the selection of suspicious sensors at the current moment. The simulation results show that the proposed algorithm can efficiently select suspicious sensors, which will greatly improve the efficiency of attack detection and ensure the security of information fusion of the power grid monitoring system. © 2023 IEEE.

关键词： Scheduling algorithms

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Modified Implicit Discretization of the Super-Twisting controller

arXiv

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arXiv 2023年

作者： Andritsch, Benedikt Watermann, Lars Koch, Stefan Reichhartinger, Markus Reger, Johann Horn, Martin The Institute of Automation and Control Graz University of Technology Graz Austria The Christian Doppler Laboratory for Model-Based Control of Complex Test Bed Systems Institute of Automation and Control Graz University of Technology Graz Austria The Control Engineering Group at Technische Universität Ilmenau Ilmenau Germany

In this paper a novel discrete-time realization of the super-twisting controller is proposed. The closed-loop system is proven to be globally asymptotically stable in the absence of a disturbance by means of Lyapunov theory. Furthermore, the steady-state error in the disturbed case is computed analytically and shown to be independent of the parameters. The steady-state error only depends on the sampling time and the unknown disturbance. The proposed discrete-time controller is compared to previously published discrete-time super-twisting controllers by means of the controller structure. In extensive simulation studies the proposed controller is evaluated comparative to known controllers. The continuous-time super-twisting controller is capable of rejecting any unknown Lipschitz-continuous perturbation. Furthermore, the convergence time decreases, if any of the gains is increased. The simulations demonstrate that the systems closed in the loop with each of the known controllers lose one of these properties, introduce discretization-chattering effects, or do not yield the same accuracy level as with the proposed controller. The proposed controller, in contrast, is beneficial in terms of the above described properties of the continuous-time super-twisting controller. © 2023, CC BY.

关键词： controllers

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Uncertain uncertainty in data-driven stochastic optimization: towards structured ambiguity sets

Uncertain uncertainty in data-driven stochastic optimization...

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IEEE Conference on Decision and control

作者： Lotfi M. Chaouach Dimitris Boskos Tom Oomen Systems and Control Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Department of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Ambiguity sets of probability distributions are a prominent tool to hedge against distributional uncertainty in stochastic optimization. The aim of this paper is to build tight Wasserstein ambiguity sets for data-driven optimization problems. The method exploits independence between the distribution components to introduce structure in the ambiguity sets and speed up their shrinkage with the number of collected samples. Tractable reformulations of the stochastic optimization problems are derived for costs that are expressed as sums or products of functions that depend only on the individual distribution components. The statistical benefits of the approach are theoretically analyzed for compactly supported distributions and demonstrated in a numerical example.

关键词： Uncertainty Costs Cost function Probability distribution Random variables

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ADRC Combined with IMC for Non-Minimum Phase systems

ADRC Combined with IMC for Non-Minimum Phase Systems

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第42届中国控制会议

作者： Xiangyang Li Xindi Deng Senping Tian Wei Ai Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of Technology

The integrated control method,active disturbance rejection control(ADRC) combined with internal model control(IMC),is proposed for non-minimum phase *** ADRC combined with IMC is to reduce the influence of zeros on right half plant(RHP),system uncertainty and outer *** improved IMC is put in the inner loop and ADRC is adopted in the outer *** order to enhance the system's stability,an approximate zero phase error model of controlled plant(ZPEM) is used as the internal model in the IMC control loop where the feedback of the inner loop is the dynamic weighted filter sum of the real output and the ZPEM *** theoretical analysis and simulations verify effectiveness of the proposed method for non-minimum phase systems.

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Unraveling tensor structures in correct-by-design controller synthesis

arXiv

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arXiv 2025年

作者： Wang, Ruohan Sun, Zhiyong Haesaert, Sofie Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Netherlands Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China

Formal safety guarantees on the synthesis of controllers for stochastic systems can be obtained using correct-by-design approaches. These approaches often use abstractions as finite-state Markov Decision Processes (MDPs). As the state space of these MDPs grows, the curse of dimensionality makes the computational and memory cost of the probabilistic guarantees, quantified with dynamic programming, scale exponentially. In this work, we leverage decoupled dynamics and unravel, via dynamic programming operations, a tree structure in the Canonical Polyadic Decomposition (CPD) of the value functions. For discrete-time stochastic systems with syntactically co-safe linear temporal logic (scLTL) specifications, we provide provable probabilistic safety guarantees and significantly alleviate the computational burden. We provide numerical studies and validations of the theoretical results on several typical case studies and showcase that the uncovered tree structure enables efficient reductions in the computational burden. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Research on Data Standardization and Unified Data Interface Based on Digital Station System 5

Research on Data Standardization and Unified Data Interface ...

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5th IEEE Advanced Information Management, Communicates, Electronic and Automation control Conference, IMCEC 2022

作者： Xiaojin, Wang Shucai, Sun Yehua, Xie Tao, Jiang Hongkun, Liu NARI TECHNOLOGY CO. LTD NARI-TECH Nanjing Control Systems Ltd. NARI Group Corporation State Grid Electric Power Research Institute Nanjing China

ISBN: (纸本)9781665479677

At present, the development of energy and power is faced with a major strategic task of ensuring safe and reliable supply, accelerating the clean and low-carbon transformation, and helping to achieve the 'dual carbon' goal. The construction of digital station information system bears the historical mission of energy transformation, has great and far-reaching significance, is an important part of a clean, low-carbon, safe and efficient energy system, and has new features such as clean, low-carbon, safe and controllable, flexible and efficient, intelligent, friendly, open and interactive. In this paper, the equipment model data standardization technology and unified data interface technology based on the digital station information system are studied to solve the problems faced in the construction of the digital station information system, such as multiple islands of information, multiple fault patterns, and low data value. According to the principle of unified collection, storage and utilization of distribution network information, the SG-CIM model specification is used as the basis for modeling to provide standardized data;The unified data interface of digital station information is formed through unified basic software and hardware facilities, unified equipment information model, and unified application and development specifications to achieve equipment centric panoramic data aggregation and sharing, and visual presentation of operation and maintenance business. Realize the coordination and interaction of digital station information system, and provide a strong support way for the country to promote the 'double carbon' goal. © 2022 IEEE.

关键词： Information systems

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Beyond Common Randomness: Quantum Resources in Decentralized control

Beyond Common Randomness: Quantum Resources in Decentralized...

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IEEE Conference on Decision and control

作者： Shashank A. Deshpande Ankur A. Kulkarni Department of Aeronautics and Astronautics DINaMo group Massachusetts Institute of Technology Department of Systems and Control Engineering Indian Institute of Technology Bombay Mumbai

Ananthram and Borkar [1] showed that there exist strategies that are consistent with the requirements of a decentralized information structure but are unattainable through the use of common randomness. This opens the question of discovering physically realisable mechanisms that provide access to this region of the strategic space. In our previous work we introduced a class of quantum strategies that allow such access in a two-agent setting. In this paper, we consider the problem of optimal allocation of a $k$ -partite quantum resource amongst $n$ agents, $k < n$ . We study the problem of decentralized estimation of a binary source by agents that are informed through independent binary symmetric channels, and face a cost that is homogeneous in their actions. We show a $k$ -partite quantum resource produces the maximum advantage over classical strategies when allocated to the agents with the $k$ most reliable channels.

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd International Conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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