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Distributed Least Squares Solver for Network Linear Equations

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arXiv 2018年

作者： Yang, Tao George, Jemin Qin, Jiahu Yi, Xinlei Wu, Junfeng State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China U.S. Army Research Laboratory AdelphiMD20783 United States Department of Automation University of Science and Technology of China Hefei230027 China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In this paper, we study the problem of finding the least square solutions of over-determined linear algebraic equations over networks in a distributed manner. Each node has access to one of the linear equations and holds a dynamic state. We first propose a distributed least square solver over connected undirected interaction graphs and establish a necessary and sufficient on the step-size under which the algorithm exponentially converges to the least square solution. Next, we develop a distributed least square solver over strongly connected directed graphs and show that the proposed algorithm exponentially converges to the least square solution provided the step-size is sufficiently small. Moreover, we develop a finite-time least square solver by equipping the proposed algorithms with a finite-time decentralized computation mechanism. The theoretical findings are validated and illustrated by numerical simulation examples. Copyright © 2018, The Authors. All rights reserved.

关键词： Linear equations

Author Correction: Frontotemporal coordination predicts working memory performance and its local neural signatures

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Nature communications 2021年第1期12卷 3811页

作者： Ehsan Rezayat Mohammad-Reza A Dehaqani Kelsey Clark Zahra Bahmani Tirin Moore Behrad Noudoost School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran. School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran. dehaqani@ut.ac.ir. Cognitive Systems Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran. dehaqani@ut.ac.ir. Department of Ophthalmology and Visual Sciences University of Utah Salt Lake City UT USA. Department of Biomedical Engineering Tarbiat Modares University Tehran Iran. Department of Neurobiology Stanford University Stanford CA USA. Howard Hughes Medical Institute Stanford University Stanford CA USA. Department of Ophthalmology and Visual Sciences University of Utah Salt Lake City UT USA. behrad.noudoost@utah.edu.

Risk-Limiting Unit Commitment in Smart Grid with Intelligent Periphery

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IEEE Transactions on Power systems 2017年第6期32卷 4696-4707页

作者： Peng, Chaoyi Hou, Yunhe Yu, Nanpeng Wang, Weisheng Department of Electrical and Electronic Engineering University of HongKong Pokfulam Hong Kong HKU Shenzhen Institute of Research and Innovation Shenzhen518057 China Department of Electrical and Computer Engineering University of California Riverside RiversideCA92521 United States State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing100085 China

This paper proposes the risk-limiting unit commitment (RLUC) as the operational method to address the uncertainties in the smart grid with intelligent periphery (GRIP). Three key requirements are identified for the RLUC in GRIP. The first one requires the RLUC to be modeled as a multistage multiperiod unit commitment problem considering power trades, operational constraints, and operational risks. The second one requires the RLUC considering the conditional prediction to achieve a globally optimal solution. It is addressed by using conditional probability in a scenario-based form. The last one requires the risk index in the RLUC to be both valid and computationally friendly, and it is tackled by the utilization of a coherent risk index and the mathematical proof of a risk chain theorem. Finally, the comprehensive RLUC in GRIP satisfying all the three requirements is solved by an equivalent transformation into a mixed integer piecewise linear programming problem. Case studies on a nine-bus system, a realistic provincial power system, and a regional power grid in China demonstrate the advantages of the proposed RLUC in GRIP. © 2017 IEEE.

关键词： Smart power grids

Sensorless nonlinear control of fed-batch Escherichia coli cultivation bioprocess using the state-dependent approach 12th

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12th Portuguese Conference on Automatic control, controlO 2016

作者： Iratni, Abdelhamid Araújo, Rui Rastegar, Saeid Mostefai, Mohammed Faculty of Science and Technology Electrical Engineering Department University of Bordj Bou Arreridj El Anasser Algeria Department of Electrical and Computer Engineering Institute for Systems and Robotics University of Coimbra Coimbra Portugal Automatic Control Laboratory of Setif Electrical Engineering Department Faculty of Technology University of Setif1 Sétif Algeria

ISBN: (纸本)9783319436708

The cultivation of Escherichia coli bacteria is widely used by geneticists and biopharmaceuticals to produce medicines and vaccines. In such industries, Bioprocesses are known by their harsh environment and very expensive physical sensors. This paper deals with nonlinear estimation and control of fed-batch Escherichia coli bioprocess. Enhancing the process performance and maximizing its efficiency by optimizing the control of Escherichia coli cultures depends on the availability of appropriate online sensors for the main culture components. In this study, an innovative method for nonlinear estimation and control of bioprocesses is designed and assessed. The new algorithm for control and estimation of Escherichia coli bioprocess states, based on the State-Dependent Riccati Equation technique, is validated by numerical simulation. The results obtained clearly demonstrate the effectiveness of the proposed technique for Biosystems in measuring, monitoring, and control of their natural nonlinear dynamics. © Springer International Publishing Switzerland 2017.

关键词： Extended Kalman filters

Robustness of Interdependent Random Geometric Networks

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arXiv 2017年

作者： Zhang, Jianan Yeh, Edmund Modiano, Eytan Laboratory for Information and Decision Systems Massachusetts Institute of Technology Electrical and Computer Engineering Department Northeastern University

—We propose an interdependent random geometric graph (RGG) model for interdependent networks. Based on this model, we study the robustness of two interdependent spatially embedded networks where interdependence exists between geographically nearby nodes in the two networks. We study the emergence of the giant mutual component in two interdependent RGGs as node densities increase, and define the percolation threshold as a pair of node densities above which the giant mutual component first appears. In contrast to the case for a single RGG, where the percolation threshold is a unique scalar for a given connection distance, for two interdependent RGGs, multiple pairs of percolation thresholds may exist, given that a smaller node density in one RGG may increase the minimum node density in the other RGG in order for a giant mutual component to exist. We derive analytical upper bounds on the percolation thresholds of two interdependent RGGs by discretization, and obtain 99% confidence intervals for the percolation thresholds by simulation. Based on these results, we derive conditions for the interdependent RGGs to be robust under random failures and geographical attacks. Copyright © 2017, The Authors. All rights reserved.

关键词： Solvents

The Robust Exact Differentiator Toolbox: Improved Discrete-Time Realization

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The Robust Exact Differentiator Toolbox: Improved Discrete-T...

International Workshop on Variable Structure systems, VSS

作者： M. Reichhartinger S. Koch H. Niederwieser S.K. Spurgeon Institute of Automation and Control Graz University of Technology Graz Austria Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Graz University of Technology Graz Austria Information and Computer Engineering Graz University of Technology Graz Austria Department of Electronic & Electrical Engineering University College London London UK

This paper presents a new release of A Robust Exact Differentiator Toolbox for Matlab ® /Simulink ® proposed in [1]. This release features a new discrete-time realization of the continuous-time robust exact differentiator. The implemented discretization scheme is less sensitive to gain overestimation and does not suffer from the discretization chattering effect. Hence, the single tuning parameter of the new version of the implemented differentiator is more intuitive to tune. Furthermore, it shows superior estimation performance in the case of large sampling times in comparison to the previous release. This is confirmed by the presented results obtained by numerical simulations and a real world application.

关键词： Eigenvalues and eigenfunctions Tuning Estimation error Robustness Mathematical model Real-time systems

Gaussian Graphical Models: An Algebraic and Geometric Perspective

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arXiv 2017年

作者： Uhler, Caroline Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Institute for Data Systems and Society Massachusetts Institute of Technology

Gaussian graphical models are used throughout the natural sciences, social sciences, and economics to model the statistical relationships between variables of interest in the form of a graph. We here provide a pedagogic introduction to Gaussian graphical models and review recent results on maximum likelihood estimation for such models. Throughout, we highlight the rich algebraic and geometric properties of Gaussian graphical models and explain how these properties relate to convex optimization and ultimately result in insights on the existence of the maximum likelihood estimator (MLE) and algorithms for computing the MLE. Copyright © 2017, The Authors. All rights reserved.

关键词： Gaussian distribution

Separable convex optimization with nested lower and upper constraints

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arXiv 2017年

作者： Vidal, Thibaut Gribel, Daniel Jaillet, Patrick Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Operations Research Center Massachusetts Institute of Technology

We study a convex resource allocation problem in which lower and upper bounds are imposed on partial sums of allocations. This model is linked to a large range of applications, including production planning, speed optimization, stratified sampling, support vector machines, portfolio management, and telecommunications. We propose an efficient gradient-free divide-and-conquer algorithm, which uses monotonicity arguments to generate valid bounds from the recursive calls, and eliminate linking constraints based on the information from sub-problems. This algorithm does not need strict convexity or differentiability. It produces an ∊-approximate solution for the continuous problem in O(n log m log nB ∊ ) time and an integer solution in O(n log m log B) time, where n is the number of decision variables, m is the number of constraints, and B is the resource bound. A complexity of O(n log m) is also achieved for the linear and quadratic cases. These are the best complexities known to date for this important problem class. Our experimental analyses confirm the good performance of the method, which produces optimal solutions for problems with up to 1,000,000 variables in a few seconds. Promising applications to the support vector ordinal regression problem are also investigated. Copyright © 2017, The Authors. All rights reserved.

关键词： Convex optimization

Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses

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arXiv 2018年

作者： Petković, Tomislav Puljiz, David Marković, Ivan Hein, Björn University of Zagreb Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Laboratory for Autonomous Systems and Mobile Robotics Unska 3 ZagrebHR-10000 Croatia Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Intelligent Process Automation and Robotics Lab Kaiserstraße 12 Deutschland KarlsruheDE-76131

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results. Copyright © 2018, The Authors. All rights reserved.

关键词： Virtual reality