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arXiv 2019年

作者： Su, Yanxu Shi, Yang Sun, Changyin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Southeast University Nanjing210096 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 3P6 Canada

This paper analyzes the contraction of the primaldual gradient optimization via contraction theory in the context of discrete-time updating dynamics. The contraction theory based on Riemannian manifolds is first established for convergence analysis of a convex optimization algorithm. The equality and inequality constrained optimization cases are studied, respectively. Under some reasonable assumptions, we construct the Riemannian metric to characterize a contraction region. It is shown that if the step-sizes of the updating dynamics are properly designed, the convergence rates for both cases can be obtained according to the contraction region in which the convergence can be ***, the augmented Lagrangian function which is projection free is adopted to tackle the inequality constraints. Some numerical experiments are simulated to demonstrate the effectiveness of the presented contraction analysis results on primal-dual gradient optimization algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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Distributed model predictive control under inexact primal-dual gradient optimization based on contraction analysis

arXiv

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arXiv 2019年

This paper develops a distributed model predictive control (DMPC) strategy for a class of discrete-time linear systems with consideration of globally coupled constraints. The DMPC under study is based on the dual problem concerning all subsystems, which is solved by means of the primal-dual gradient optimization in a distributed manner using Laplacian consensus. To reduce the computational burden, the constraint tightening method is utilized to provide a capability of premature termination with guaranteeing the convergence of the DMPC optimization. The contraction theory is first adopted in the convergence analysis of the primal-dual gradient optimization under discrete-time updating dynamics towards a nonlinear objective function. Under some reasonable assumptions, the recursive feasibility and stability of the closed-loop system can be established under the inexact solution. A numerical simulation is given to verify the performance of the proposed strategy. Copyright © 2019, The Authors. All rights reserved.

关键词： Closed loop systems

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Transient stability of the grid integrated with wind turbines during fault condition 7

Transient stability of the grid integrated with wind turbine...

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7th Brazilian Electrical systems Symposium, SBSE 2018

作者： Marcillo, Kevin Eduardo Lucas Barra, Hugo Menezes Barra, Walter Rocha, Erick Melo Nunes, Marcus Vinicius Alves Federal University of Pará Department of Electrical Engineering Automation Laboratory Belém PA66075110 Brazil Federal University of Pará Department of Electrical Engineering Power System Control Laboratory Belém PA66075110 Brazil Federal University of Pará Department of Electrical Engineering High Voltage Laboratory Belém PA66075110 Brazil

ISBN: (纸本)9781538633632

In this paper, a computational study is carried out in order to assess the impacts of the wind power generation on dynamic behavior in the power system concerned by a modelling simulation. The concepts of different types of wind turbine generators (WTGs) and the principles of the grid-connected structures of wind power generation systems are first briefly introduced. The Electrical Network used for the investigations comes from the IEEE. A change was made to include the Wind Power system with Wind Power Generators in Bar 2, as described later. As a comparison, the paper reports the results of several tests by using different WTG technologies, evaluating the corresponding performance under the occurrence of the same kind of fault. © 2018 IEEE.

关键词： Wind Turbine Generators

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A gradient algorithm for Hamiltonian identification of open quantum systems

arXiv

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arXiv 2019年

作者： Xue, Shibei Wu, Rebing Li, Dewei Jiang, Min Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Automation Tsinghua University Beijing100084 China Center for Quantum Information Science and Technology BNRist Beijing100084 China School of Electronics and Information Engineering Soochow University Suzhou215006 China

In this paper, we present a gradient algorithm for identifying unknown parameters in an open quantum system from the measurements of time traces of local observables. The open system dynamics is described by a general Markovian master equation based on which the Hamiltonian identification problem can be formulated as minimizing the distance between the real time traces of the observables and those predicted by the master equation. The unknown parameters can then be learned with a gradient descent algorithm from the measurement data. We verify the effectiveness of our algorithm in a circuit QED system described by a Jaynes-Cumming model whose Hamiltonian identification has been rarely considered. We also show that our gradient algorithm can learn the spectrum of a non-Markovian environment based on an augmented system model. Copyright © 2019, The Authors. All rights reserved.

关键词： Hamiltonians

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Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

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SENSORS 2020年第3期20卷 726-726页

作者： Lopez-Vazquez, Vanesa Lopez-Guede, Jose Manuel Marini, Simone Fanelli, Emanuela Johnsen, Espen Aguzzi, Jacopo DS Labs R+D+I unit of Deusto Sistemas S.A. 01015 Vitoria-Gasteiz Spain. Department of System Engineering and Automation Control Faculty of Engineering of Vitoria-Gasteiz University of the Basque Country (UPV/EHU) Nieves Cano 12 01006 Vitoria-Gasteiz Spain. Institute of Marine Sciences National Research Council of Italy (CNR) 19032 La Spezia Italy. Stazione Zoologica Anton Dohrn (SZN) Naples 80122 Italy. Department of Life and Environmental Sciences Polytechnic University of Marche Via Brecce Bianche 60131 Ancona Italy. Institute of Marine Research PO Box 1870 5817 Bergen Norway. Instituto de Ciencias del Mar (ICM) of the Consejo Superior de Investigaciones Científicas (CSIC) 08003 Barcelona Spain.

An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.

关键词： cabled observatories artificial intelligence deep learning machine learning deep-sea fauna

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The NRSF-SVM based method for nonlinear rotor bearing fault diagnosis

The NRSF-SVM based method for nonlinear rotor bearing fault ...

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第30届中国控制与决策会议

作者： Ying Ma Zhong Luo Haopeng Liu Yunpeng Zhu School of Mechanical Engineering & Automation Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern University Department of Automatic Control and System Engineering University of Sheffield

The fault diagnosis scheme of the rotor bearing system often conducted by using either signal analysis approach or modeling method. In practice, the structure of the rotor bearing system is complex and contains many nonlinear factors. Therefore, it is hard to use the model-based method for fault detection. Thus, signal analysis approach is more efficient. In the signal analysis approach, frequency response function is widely applied. However, the existing analyzing methods of frequency response function have some limitations, such as multidimensional property. Thus, in this study, the concept of Nonlinear Response Spectrum Function(NRSF) is proposed to solve the problem. Finally, a simulation is conducted to identify the multi-fault rotor bearing systems by the proposed NRSFs feature and Support Vector Machine(SVM) classifier, showing that the NRSF-SVM approach has an excellent performance in fault identification of rotor bearing system.

关键词： rotor bearing system fault diagnosis NRSF SVM

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Semi-global leader-following output consensus of discrete-time linear multi-agent systems with input saturation

Semi-global leader-following output consensus of discrete-ti...

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2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018

作者： Shi, Liangren Li, Yuanlong Lin, Zongli Brown, Charles L. Innovation Center Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Department of Automation Shanghai Jiao Tong University China Department of Electrical and Computer Engineering University of Virginia P.O. Box 400743 CharlottesvilleVA22904-4743 United States

ISBN: (纸本)9781538668689

We consider the problem of semi-global leader-following output consensus for a group of agents, consisting of a leader agent and some follower agents, which are described by discrete-time linear systems and connected on a directed network. For each follower agent, a distributed observer is built that estimates the state of the leader. Based on the estimates of the leader state and the solution of a parameterized algebraic Riccati equation, we design low gain consensus protocols that are shown to solve the semi-global output consensus problem as long as the leader agent is reachable globally on the network. Numerical simulation shows the performance of the proposed consensus protocols. © 2018 IEEE.

关键词： Multi agent systems

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Robust MPC for constrained uncertain systems with data-driven improvement

Robust MPC for constrained uncertain systems with data-drive...

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第37届中国控制会议

作者： Lingyi Yang Dewei Li Jianbo Lu Yugeng Xi Bin Li Department of Automation Shanghai Jiaotong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education Department of Automation Sichuan UniversitySchool of Electrical Engineering and Information

The adaptive model predictive control is regarded as an effective control method for unknown constrained ***, the adaptive model predictive control needs model identification. The process of identification demands a certain amount of data and places some requirements on data which makes it difficult to be implemented. To handle it, this paper proposes a novel data-driven approach without model identification. The data of previous time is utilized to describe state space and input space of the uncertain system instead of identifying the true model. Based on the data-driven method, the Quasi-MinMax control strategy is used to design the robust data-driven MPC controller which directly calculates the input from the past data. Combined with the data-driven method, a free control variable is introduced to compensate for the insufficiency of past data. It is shown that adopting the data-driven controller can reduce conservatism by lessening model uncertainty and improve control performance. Meanwhile, the proposed design is proven to be recursively feasible and stabilizing. A numerical example demonstrates the effectiveness and advantages of the proposed control method.

关键词： Adaptive learning control Data-driven approach Model predictive control

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Short term electric load forecast with artificial neural networks

arXiv

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arXiv 2018年

作者： Cristian, Vasar Iosif, Szeidert Ioan, Filip Gabriela, Prostean Department of Control System Engineering Politehnica' University from Timisoara Faculty of Automation and Computer Science Timisoara300223 Romania

This paper presents issues regarding short term electric load forecasting using feedforward and Elman recurrent neural networks. The study cases were developed using measured data representing electrical energy consume from Banat area. There were considered 35 different types of structure for both feedforward and recurrent network cases. For each type of neural network structure were performed many trainings and best solution was selected. The issue of forecasting the load on short term is essential in the effective energetic consume management in an open market environment. Copyright © 2007 IFAC Copyright © 2018, The Authors. All rights reserved.

关键词： Electric load forecasting

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Joint beamformer design for wireless fronthaul and access links in C-RANs

Joint beamformer design for wireless fronthaul and access li...

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作者： Hu, Bin Hua, Cunqing Chen, Cailian Guan, Xinping Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China

This paper presents a joint design framework of fronthaul and access links in cloud radio access networks, wherein the fronthaul data delivery between the central processor (CP) and small-cell base stations (SBSs) is carried over wireless links, which is more cost effective and flexible than the conventional wired fronthaul solutions. In this framework, the coordinated beamforming scheme is adopted by the SBSs to serve the users cooperatively, which not only reduces the bandwidth requirement over the fronthaul links, but also eliminates the inter-cell interference in the access links. To further exploit the spatial diversity over the fronthaul links, the multi-user beamforming scheme is adopted by the CP to deliver the data of all users to their serving SBSs simultaneously. This non-convex and combinatorial optimization problem is reformulated to a unified beamformer design problem using the $\ell -{0}/\ell -{1}$ -norm framework, which is still difficult to obtain the optimal solutions. Therefore, we first propose an algorithm based on the difference of convex (DC) programming scheme to find the suboptimal solutions, whereby the formulated problem is transformed to the standard DC programming problem and solved iteratively using the convex-concave procedure algorithm. We then propose another algorithm based on the successive convex approximation and weighted minimum-mean-squared-error approaches, and solve the transformed problem using the block coordinate update scheme. The pros and cons of these two algorithms are discussed, and simulation results are provided to demonstrate the performance gain of our schemes over other benchmark schemes. © 2018 IEEE.

关键词： Beamforming

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