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arXiv 2020年

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M.A. Yazdanpanah, Mohammad Javad Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

Motivated by the inadequacy of the existing control strategies for power systems affected by time-varying uncontrolled power injections such as loads and the increasingly widespread renewable energy sources, this paper proposes two control schemes based on the well-known output regulation control methodology. The first one is designed based on the classical output regulation theory and addresses the so-called Load Frequency control (LFC) problem in presence of time-varying uncontrolled power injections. Then, in order to also minimize the generation costs, we use an approximate output regulation method that solves numerically only the partial differential equation of the regulator equation and propose a controller based on this solution, minimizing an appropriate penalty function. An extensive case study shows excellent performance of the proposed control schemes in different and critical scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Scheduling

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An Improved TDoA Localization Algorithm Based on AUV for Underwater Acoustic Sensor Networks 1st

An Improved TDoA Localization Algorithm Based on AUV for Und...

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1st EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019

作者： Yu, Kaicheng Hao, Kun Li, Cheng Du, Xiujuan Wang, Beibei Liu, Yonglei School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Qinghai Normal University XiningQinghai810008 China School of Control and Mechanical Engineering Tianjin Chengjian University Tianjin300384 China

ISBN: (纸本)9783030229672

Now localization is one of the major issues in underwater environment work. In terrestrial application, time different of arrival (TDoA) localization algorithm has been widely used. However, most localization systems rely on radio or optical signals while they cannot propagate well in water. Therefore, with complicated environment in underwater acoustic sensor networks (UASNs), traditional TDoA localization algorithm suffers various unstable factors, such as they can only work in a finite region or need clock synchronization. In this paper, we propose an improved TDoA localization algorithm (ITLA) based on AUV for UASNs. The mobile AUV first finds its own accurate three-dimensional coordinates in the surface with the help of GPS or other terrestrial location systems. Then we deployed AUV at predefined depth in underwater as reference nodes. AUV periodically sends packets with coordinates information to unlocalized nodes in different positions. After receiving data and a series of calculation, we quantify the conditions for unique localization and propose another condition to evaluate the reliability of results. This algorithm can achieve relatively higher accuracy with relatively smaller calculation and overcome some traditional localization drawbacks. We demonstrate the trade-offs between location coverage, the cost in placing reference nodes, and energy consumption. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2019.

关键词： Autonomous underwater vehicles

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Compositional Synthesis of Symbolic Models for Infinite Networks ⁎

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IFAC-PapersOnLine 2020年第2期53卷 1868-1873页

作者： Abdalla Swikir Navid Noroozi Majid Zamani Department of Electrical and Computer Engineering Technical University of Munich Germany Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Germany Computer Science Department University of Colorado BoulderUSA. Institute of Informatics Ludwig Maximilian University Munich Germany

In this paper, we provide a compositional method for the construction of symbolic models (a.k.a. finite abstractions) for infinite networks of discrete-time control systems. The concrete infinite network and its symbolic model are related by a so-called alternating simulation function which allows one to quantify the mismatch between the output behavior of the infinite interconnection of concrete subsystems and that of their symbolic models. We show that such an alternating simulation function can be obtained compositionally by assuming some small-gain type conditions and composing so-called local alternating simulation functions constructed for subsystems. Assuming certain stability property of concrete subsystems, we also provide a technique to synthesize their symbolic models together with their corresponding local alternating simulation functions. Finally, we apply our results to a traffic network divided into infinitely many cells.

关键词： Compositionality interconnected systems small-gain condition infinite networks symbolic models

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Passivity properties for regulation of DC networks with stochastic load demand

arXiv

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arXiv 2020年

In this paper we present new (stochastic) passivity properties for Direct Current (DC) power networks, where the unknown and unpredictable load demand is modelled by a stochastic process. More precisely, the considered power network consists of distributed generation units supplying ZIP loads, i.e., nonlinear loads comprised of impedance (Z), current (I) and power (P) components. Differently from the majority of the results in the literature, where each of these components is assumed to be constant, we consider time-varying loads whose dynamics are described by a class of stochastic differential equations. Finally, we prove that an existing distributed control scheme achieving current sharing and (average) voltage regulation ensures the asymptotic stochastic stability of the controlled network. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power transmission networks

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A federated data-driven evolutionary algorithm

arXiv

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arXiv 2021年

作者： Xu, Jinjin Jin, Yaochu Du, Wenli Gu, Sai Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom Department of Chemical and Process Engineering University of Surrey GuildfordGU27XH United Kingdom

Data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems. However, existing data-driven optimization algorithms require that all data are centrally stored, which is not always practical and may be vulnerable to privacy leakage and security threats if the data must be collected from different devices. To address the above issue, this paper proposes a federated data-driven evolutionary optimization framework that is able to perform data driven optimization when the data is distributed on multiple devices. On the basis of federated learning, a sorted model aggregation method is developed for aggregating local surrogates based on radial-basis-function networks. In addition, a federated surrogate management strategy is suggested by designing an acquisition function that takes into account the information of both the global and local surrogate models. Empirical studies on a set of widely used benchmark functions in the presence of various data distributions demonstrate the effectiveness of the proposed framework. © 2021, CC BY.

关键词： Optimization

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Generating infrared image from visible image using Generative Adversarial Networks

Generating infrared image from visible image using Generativ...

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2019 IEEE International Conference on Unmanned Systems, ICUS 2019

作者： An, Lei Zhao, Jiajia Di, Huijun Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Coorperation Laboratory Beijing China

ISBN: (纸本)9781728137926

Infrared images have properties that are unaffected by illumination compared to visible images, object can be clearly recognized at day or night. Therefore, it is a better choice to use infrared images when training deep learning algorithms in unmanned systems. However, how to obtain a large number of infrared images makes it possible to train deep learning algorithm, which is the focus of our attention. In contrast to difficulties in direct shooting, computer simulations can generate large infrared images from visible images. But models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real infrared images, we exploit recent advances in paired image-to-image translation. We extend Pixel2Pixel to generate infrared image from visible image and propose a novel way of multilayer semantic information fusion, which significantly improves the quality of generated images. Finally, the image generated by our proposed PixelPro network is almost consistent with the real image distribution, achieved the purpose of expanding the dataset. © 2019 IEEE.

关键词： Generative adversarial networks

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Promela and Spin Formal Verification of an M-Health Medical Social Media System

Promela and Spin Formal Verification of an M-Health Medical ...

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2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019

作者： Al-Gayar, Sarmad Monadel Sabree Goga, Nicolae Al-Habeeb, Naseer Abdulkarim Jaber Doctoral School of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania Faculty of Engineering in Foreign Languages Computer Science University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781538680100

The process of detecting and identifying errors early in the life-cycle of any software has many challenges. The tools used for model checking are however becoming more effective and usable because they are helping the identification of errors. This has empowered users to apply model checking to large-scale problems. The process of validating the model implementation is normally harder. We created a Promela model by using a model checker called Spin in order to verify the Medical Social Media System based on Social Oriented Networks by using M-Health technology and sensors in smartphones and bracelets for medical data acquisition, in order for it to be used in the healthcare sector in Iraq. For the Promela Model, we first described the behaviors of the Medical Social Media Systems via UML timelines. After that, we combined the UML timelines in state diagrams that were finally transformed into a Promela model and verified with the Spin model checker. © 2019 IEEE.

关键词： Model checking

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Hidden Convexity in Robust Waveform and Receive Filter Bank Optimization for Range Unambiguous Clutter

Hidden Convexity in Robust Waveform and Receive Filter Bank ...

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IEEE International Conference on Radar

作者： Augusto Aubry Guolong Cui Antonio De Maio Xiaolin Du Università degli Studi di Napoli “Federico II” Italy School of Information and Communication Engineering University of Electronic Science and Technology of China China School of Computer and Control Engineering Yantai University China

ISBN: (数字)9781728168135

ISBN: (纸本)9781728168142

This paper deals with the robust joint design of a radar transmit waveform and receive filter bank in a background of range unambiguous signal-dependent clutter. Assuming an unknown Doppler shift for the target, the worst-case signal-to-interference-plus-noise-ratio (SINR) at the output of the receive filter bank is considered as the figure of merit. The transceiver design is pursued considering a max-min optimization problem with some constraints on the transmit energy, similarity, and signal dynamic range. Hidden convexity is shown and a procedure to derive optimal waveform and filters is given. Simulation results highlight the effectiveness of the devised method.

关键词：

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Nonlinear Curvature Modeling for MPC of Autonomous Vehicles*

Nonlinear Curvature Modeling for MPC of Autonomous Vehicles*

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International Conference on Intelligent Transportation

作者： Gonçalo Collares Pereira Pedro F. Lima Bo Wahlberg Henrik Pettersson Jonas Mårtensson Research and Development Scania CV AB Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

This paper investigates how to compensate for curvature response mismatch in lateral Model Predictive control (MPC) of an autonomous vehicle. The standard kinematic bicycle model does not describe accurately the vehicle yaw-rate dynamics, leading to inaccurate motion prediction when used in MPC. Therefore, the standard model is extended with a nonlinear function that maps the curvature response of the vehicle to a given request. Experimental data shows that a two Gaussian functions approximation gives an accurate description of this mapping. Both simulation and experimental results show that the corresponding modified model significantly improves the control performance when using Reference Aware MPC for autonomous driving of a Scania heavy-duty construction truck.

关键词： Kinematics Bicycles Autonomous vehicles Computational modeling Vehicle dynamics Predictive models Standards

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The Concept Information of Graph Granule with Application to Embedding Learning of Knowledge Graph

Research Square

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Research Square 2021年

作者： Niu, Jiaojiao Chen, Degang Ma, Yinglong Li, Jinhai North China Electric Power University Beijing Campus China Kunming University of Science and Technology Lianhua Campus China School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Mathematics and Physics North China Electric Power University Beijing102206 China Data Science Research Center Kunming University of Science and Technology Yunnan Kunming650500 China

Knowledge graph embedding (KGE) are routinely used to represent entities and their relations in knowledge bases with a quantitative measure, and the triples usually play the role of basic units in KGE learning. Considering that triples are sometimes far from adequate and knowledge graph itself contains a lot of information, this paper employs FCA-based technology to mine the deterministic knowledge from knowledge graph, that is, the formal concept, and attempts to establish the relationship between knowledge graph (KG) and formal concept analysis (FCA). Specifically, each set of triples sharing the same head entity are grouped as a graph granule and the concepts of each graph granule are mined. By further exploration, the maximal concepts are integrated into embedding learning to develop a novel KGE model named TransGr for knowledge graph completion. This model learns a matrix for each maximal concept in graph granule as well as a vector for each entity and relation. The performed experiments on link prediction and triple classification tasks demonstrate that the proposed TransGr model is effective on the datasets with relatively complete graph granules. © 2021, CC BY.

关键词： Formal concept analysis

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