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Mediterranean Conference on control and Automation (MED)

作者： Grigoris Michos Pablo R. Baldivieso-Monasterios George C. Konstantopoulos Department of Automatic Control &amp Systems Engineering University of Sheffield Sheffield UK Department of Electrical and Computer Engineering University of Patras Rion Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This work proposes a distributed robust control architecture for meshed DC Microgrid networks. Each interlinking converter is modelled as a network node and is connected in parallel to a constant power load representing the network’s power consumption. Each node employs a local controller consisting of two parts; current regulation based on a modified version of the state-limiting PI and a distributed MPC driving the system to desired setpoints. We analytically prove each controller’s robustness to model variations caused by changes in both the power demand and the transmitted information among the subsystems. The concept of positive invariance sets and the inherent robustness properties of the nominal MPC are used to prove recursive feasibility of the optimal control problem and guarantee constraint satisfaction at all times. The stability proof of the cascaded node dynamics is based upon the emerging properties of both the state limiting PI and the distributed MPC design. Demonstration of the results is given in a simulated scenario.

关键词： Current control Uncertain systems Power demand Decentralized control Microgrids Robustness Stability analysis

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Navigating A Mobile Robot Using Switching Distributed Sensor Networks

Navigating A Mobile Robot Using Switching Distributed Sensor...

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European control Conference (ECC)

作者： Xingkang He Ehsan Hashemi Karl H. Johansson Ericsson AB Sweden Department of Mechanical Engineering University of Alberta Canada Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper proposes a method to navigate a mobile robot by estimating its state over a number of distributed sensor networks (DSNs) such that it can successively accomplish a sequence of tasks, i.e., its state enters each targeted set and stays inside no less than the desired time, under a resource-aware, time-efficient, and computation-and communication-constrained setting. We propose a new robot state estimation and navigation architecture, which integrates an event-triggered task-switching feedback controller for the robot and a two-time-scale distributed state estimator for each sensor. With the controller, the robot is able to accomplish a task by following a reference trajectory and switch to the next task when an event-triggered condition is fulfilled. With the estimator, each active sensor is able to estimate the robot state. We provide conditions to ensure that the state estimation error and the trajectory tracking deviation are upper bounded by two time-varying sequences, respectively. Furthermore, we find a sufficient condition for accomplishing a task and provide an upper bound of running time for the task. Numerical simulations of an indoor robot’s localization and navigation are provided to validate the proposed architecture

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Compressive Sensing-Based Reconstruction of Lissajous-Like Nodding Lidar Data

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ASME Letters in Dynamic systems and control 2021年第1期1卷 011009页

作者： Benson, Michael T. Sathishchandra, Harish Clayton, Garrett M. Andersson, Sean B. Department of Mechanical Engineering Villanova University VillanovaPA19085 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Center for Nonlinear Dynamics and Control Department of Mechanical Engineering Villanova University VillanovaPA United States Department of Mechanical Engineering Division of Systems Engineering Boston University BostonMA United States

In this article, a compressive sensing-based reconstruction algorithm is applied to data acquired from a nodding multibeam Lidar system following a Lissajous-like trajectory. Multibeam Lidar systems provide 3D depth information of the environment, but the vertical resolution of these devices may be insufficient in many applications. To mitigate this issue, the Lidar can be nodded to obtain higher vertical resolution at the cost of increased scan time. Using Lissajous-like nodding trajectories allows for the trade-off between scan time and horizontal and vertical resolutions through the choice of scan parameters. These patterns also naturally subsample the imaged area. In this article, a compressive sensing-based reconstruction algorithm is applied to the data collected during a relatively fast and therefore low-resolution Lissajous-like scan. Experiments and simulations show the feasibility of this method and compare the reconstructions to those made using simple nearest-neighbor interpolation. © 2021 ASME Letters in Dynamic systems and control. All rights reserved.

关键词： Compressed sensing

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Direct Data-Driven State-Feedback control of Linear Parameter-Varying systems

arXiv

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arXiv 2022年

作者： Verhoek, Chris Tóth, Roland Abbas, Hossam S. Control Systems group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest Hungary Institute for Electrical Engineering in Medicine Universität zu Lübeck Lübeck Germany

The framework of linear parameter-varying (LPV) systems has shown to be a powerful tool for the design of controllers for complex nonlinear systems using linear tools. In this work, we derive novel methods that allow to synthesize LPV state-feedback controllers directly from only a single sequence of data and guarantee stability and performance of the closed-loop system. We show that if the measured open-loop data from the system satisfies a persistency of excitation condition, then the full open-loop and closed-loop input-scheduling-state behavior can be represented using only the data. With this representation we formulate data-driven analysis and synthesis problems, where the latter yields controllers that guarantee stability and performance in terms of infinite horizon quadratic cost, generalized H2-norm and 2-gain of the closed-loop system. The controllers are synthesized by solving a semi-definite program. Additionally, we provide a synthesis method to handle noisy measurement data. Competitive performance of the proposed data-driven synthesis methods is demonstrated w.r.t. model-based synthesis in multiple simulation studies, including a nonlinear unbalanced disc system. © 2022, CC BY-NC-SA.

关键词： State feedback

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Data-driven Optimal Cooperative Output Regulation of Unknown Linear Discrete-time Multi-agent systems

Data-driven Optimal Cooperative Output Regulation of Unknown...

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第35届中国控制与决策会议

作者： Dong Liang Yi Dong Chaoli Wang Engang Tian Department of Control Science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University

The cooperative output regulation problem has been extensively studied on the basis of the distributed observer ***,the majority of the existing research assumes that the dynamics is known *** remove this condition,the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is ***,a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external ***,the unknown regulator equations are solved using the iterative recurrent neural network ***,the state-based data-driven distributed control law is synthesized to solve the *** optimal gains are derived by solving convex optimization problems using input and state ***,a numerical example is presented to verify the feasibility of the proposed framework.

关键词： Linear systems multi-agent systems cooperative output regulation data-driven control optimal control

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Risk-Averse Learning with Non-Stationary Distributions

arXiv

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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Yi, Xinlei Zavlanos, Michael M. Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Mechanical Engineering and Material Science Duke University DurhamNC27708 United States

Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is favorable to adopt a strategy that minimizes the negative impact of change to avoid potentially risky situations. In this paper, we investigate risk-averse online optimization where the distribution of the random cost changes over time. We minimize risk-averse objective function using the Conditional Value at Risk (CVaR) as risk measure. Due to the difficulty in obtaining the exact CVaR gradient, we employ a zeroth-order optimization approach that queries the cost function values multiple times at each iteration and estimates the CVaR gradient using the sampled values. To facilitate the regret analysis, we use a variation metric based on Wasserstein distance to capture time-varying distributions. Given that the distribution variation is sub-linear in the total number of episodes, we show that our designed learning algorithm achieves sub-linear dynamic regret with high probability for both convex and strongly convex functions. Moreover, theoretical results suggest that increasing the number of samples leads to a reduction in the dynamic regret bounds until the sampling number reaches a specific limit. Finally, we provide numerical experiments of dynamic pricing in a parking lot to illustrate the efficacy of the designed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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Distributed Optimal Load Frequency control with Stochastic Wind Power Generation

Distributed Optimal Load Frequency Control with Stochastic W...

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2021 European control Conference, ECC 2021

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M. A. Javad Yazdanpanah, Mohammad University of Groningen Jan C. Willems Center For Systems and Control ENTEG Faculty of Science and Engineering Groningen Netherlands University of Tehran Control Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Tehran Iran Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9789463842365

Motivated by the inadequacy of conventional control methods for power networks with a large share of renewable generation, in this paper we study the (stochastic) passivity property of wind turbines based on the Doubly Fed Induction Generator (DFIG). Differently from the majority of the results in the literature, where renewable generation is ignored or assumed to be constant, we model wind power generation as a stochastic process, where wind speed is described by a class of stochastic differential equations. Then, we design a distributed control scheme that achieves load frequency control and economic dispatch, ensuring the stochastic stability of the controlled network. © 2021 EUCA.

关键词： Random processes

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Analysis of Power Transformer Inrush Current in the Presence of Quasi-Direct Currents

Analysis of Power Transformer Inrush Current in the Presence...

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International Conference on Environment and electrical engineering (EEEIC)

作者： Behzad Behdani Reza Shariatinasab Mousa Afrasiabi Jamshid Aghaei Department of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical and Computer Engineering Department University of Birjand Birjand Iran Department of Electrical Engineering School of Energy Systems Lappeenranta University of Technology Lappeenranta Finland

ISBN: (数字)9781665485371

ISBN: (纸本)9781665485388

The flow of quasi-direct currents (QDCs) in AC electrical networks, is a disturbing factor that mainly prevails upon mutual impacts between different system components or due to geophysical phenomena. These QDCs can alter the normal behavior of the system components, e.g., power transformer inrush currents. In this paper, an analysis of the inrush current phenomenon in power transformers under the influence of QDCs has been performed. The effect of QDCs on power transformer inrush currents is first mathematically analyzed, and then investigated by computer simulations in EMTP-RV software. Results show that power transformer inrush currents can severely increase in the presence of QDCs.

关键词： Resistors Mathematical analysis Switches Transformer cores Behavioral sciences Steady-state Power transformers

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Technology Mapping of Genetic Circuits: From Optimal to Fast Solutions

Technology Mapping of Genetic Circuits: From Optimal to Fast...

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IEEE International Conference on computer-Aided Design

作者： Tobias Schwarz Christian Hochberger Department of Electrical Engineering and Information Technology Computer Systems Group Technische Universität Darmstadt Darmstadt Germany

Synthetic Biology aims to create biological systems from scratch that do not exist in nature. An important method in this context is the engineering of DNA sequences such that cells realize Boolean functions that serve as control mechanisms in biological systems, e.g. in medical or agricultural applications. Libraries of logic gates exist as predefined gene sequences, based on the genetic mechanism of transcriptional regulation. Each individual gate is composed of different biological parts to allow for the differentiation of their output signals. Even gates of the same logic type therefore exhibit different transfer characteristics, i.e. relation from input to output signals. Thus, simulation of the whole network of genetic gates is needed to determine the performance of a genetic circuit. This makes mapping Boolean functions to these libraries much more complicated compared to EDA. Yet, optimal results are desired in the design phase due to high lab implementation costs. In this work, we identify fundamental features of the transfer characteristic of gates based on transcriptional regulation which is widely used in genetic gate technologies. Based on this, we present novel exact (Branch-and-Bound) and heuristic (Branch-and-Bound, Simulated Annealing) algorithms for the problem of technology mapping of genetic circuits and evaluate them using a prominent gate library. In contrast to state-of-the-art tools, all obtained solutions feature a (near) optimal output performance. Our exact method only explores 6.5 % and the heuristics even 0.2 % of the design space.

关键词： Runtime Biological systems Logic gates Genetics Libraries Regulation Circuit synthesis

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