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Joint 48th IEEE Conference on decision and control and 28th Chinese control Conference

作者： Deyuan Meng Yingmin Jia Junping Du Fashan Yu the Department of Systems and Control Beihang University (BUAA) the Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and TechnologyBeijing University of Posts and Telecommunications the School of Electrical Engineering and Automation Henan Polytechnic University

This paper is concerned with the iterative learning control(ILC) problem for discrete-time systems with iterationvarying *** the so-called super-vector approach to ILC,the discrete domain bounded real lemma is employed to develop a sufficient condition ensuring both the stability and the desired H∞ performance of the ILC *** is shown that this sufficient condition can be presented in terms of linear matrix inequalities(LMIs),which can also determine the learning gain matrix.A numerical simulation example is included to validate the theoretical results.

关键词： Iterative learning control discrete-time systems iteration-varying disturbances H_∞ performance linear matrix inequality

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Particle Filter Approach to Nonlinear System Identification under Missing Observations with a Real Application

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IFAC Proceedings Volumes 2009年第10期42卷 810-815页

作者： R.Bhushan Gopaluni Thomas B. Schön Adrian G. Wills Department of Chemical and Biological Engineering University of British Columbia Vancouver Canada V6T 1Z3 (Tel: 604 827 5668 Division of Automatic Control Linköping University SE-581 83 Linköping Sweden School of Electrical Engineering and Computer Science University of New Castle Australia

This article reviews authors’ recently developed algorithm for identification of nonlinear state-space models under missing observations and extends it to the case of unknown model structure. In order to estimate the parameters in a state-space model, one needs to know the model structure and have an estimate of states. If the model structure is unknown, an approximation of it is obtained using radial basis functions centered around a maximum a posteriori estimate of the state trajectory. A particle filter approximation of smoothed states is then used in conjunction with expectation maximization algorithm for estimating the parameters. The proposed approach is illustrated through a real application.

关键词： Nonlinear systems Maximum Likelihood Parameter Estimation Expectation Maximization Particle Filters

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Stability analysis of finite-level quantized linear control systems

Stability analysis of finite-level quantized linear control ...

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European control Conference (ECC)

作者： Carlos E. de Souza Daniel F. Coutinho Minyue Fu Department of Systems and Control Laboratório Nacional de Computação Científica (LNCC/MCT) Petrópolis RJ Brazil Laboratoire d'Automatique Faculté Polytechnique de Mons Mons Belgium GACS FENG-PUCRS Brazil School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW Australia

In this paper we investigate the stability of discrete-time linear time-invariant systems subject to finite-level logarithmic quantized feedback. Both state feedback and output feedback are considered. We develop an LMI approach to estimate, for a given controller and a given finite-level quantizer, a set of admissible initial states and an associated attractor set in a neighborhood of the origin such that all state trajectories starting in the first set will converge to the attractor in a finite time and will never leave it. Furthermore, when these two such sets are a priori specified, we develop sufficient conditions for designing a suitable state or output feedback controller, along with a finite-level logarithmic quantizer.

关键词： decision support systems Europe Niobium Stability analysis control systems Bismuth Density estimation robust algorithm

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Rational controllers in computer/Communication Networks

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IFAC Proceedings Volumes 2008年第2期41卷 15427-15432页

作者： Pierre T. Kabamba Wen-Chiao Lin Semyon M. Meerkov Choon Yik Tang Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 USA Sensors & Decision Systems Group Idaho National Laboratory Idaho Falls ID 83415 USA School of Electrical and Computer Engineering University of Oklahoma Norman OK 73019 USA

Traditional feedback controllers are used to ensure system operation in a desired regime. In contrast, rational controllers are intended to determine a regime most appropriate for the system, given varying conditions of its operation. In this paper, two types of rational controllers are described and their applications in wireless personal area networks and sensor networks are presented.

关键词： Adaptation and learning control of networks sensor networks wireless networks power efficient computing/communications

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IEEE Transactions on Haptics: Editorial

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IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

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Parameter Estimation for Discrete-Time Nonlinear systems Using EM

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IFAC Proceedings Volumes 2008年第2期41卷 4012-4017页

作者： Adrian Wills Thomas B. Schön Brett Ninness School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW 2308 Australia Division of Automatic Control Linköping University SE-581 83 Linköping Sweden

In this paper we consider parameter estimation of general stochastic nonlinear state-space models using the Maximum Likelihood method. This is accomplished via the employment of an Expectation Maximisation algorithm, where the essential components involve a particle smoother for the expectation step, and a gradient-based search for the maximisation step. The utility of this method is illustrated with several nonlinear and non-Gaussian examples.

关键词： Nonlinear estimation maximum likelihood system identification expectation maximisation

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Output feedback control of linear systems with input and output quantization

Output feedback control of linear systems with input and out...

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IEEE Conference on decision and control

作者： Daniel Coutinho Minyue Fu Carlos E. de Souza Group of Automation and Control Systems Faculty of Engineering Pontifícia Universidade Católica do Rio Grande do Sul Porto Alegre Brazil School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW Australia Department of Systems and Control Laboratory Nacional de Computação Científica Petropolis Rio de Janeiro Brazil

ISBN: (纸本)9781424431236

A considerable amount of research has been done on the use of logarithmic quantizers for networked feedback control systems. However, most results are developed for the case of a single quantizer (either measurement or control signal quantization). In this paper, we investigate the case of simultaneous input and output quantization for SISO linear output feedback systems. Firstly, we show that the problem of quadratic stabilization via quantized feedback can be addressed with no conservativeness by means of the sector bound approach. Secondly, we provide a bound on the maximal admissible sector bound via a scaled H ¿ optimization problem.

关键词： Output feedback Linear feedback control systems control systems Linear systems Quantization Communication system control Feedback control Communication channels Digital control Bandwidth

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Discrete-time Intermittent Iterative Learning controller with Independent Data Dropouts

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IFAC Proceedings Volumes 2008年第2期41卷 12442-12447页

作者： Hyo-Sung Ahn Kevin L. Moore YangQuan Chen Department of Mechatronics Gwangju Institute of Science and Technology (GIST) 1 Oryong-dong Buk-gu Gwangju 500-712 Korea Division of Engineering Colorado School of Mines 1610 Illinois Street Golden CO 80401 USA Center for Self-Organizing and Intelligent Systems (CSOIS) Dept. of Electrical and Computer Engineering 4160 Old Main Hill Utah State University Logan UT 84322-4160 USA

In networked control systems (NCS), it is considered essential to design a robust controller such that the networked-system is stable against data dropouts during the network transfer. It has been shown that there is a critical data dropout rate over which the networked-system could be unstable; hence the desired task cannot be achieved. This paper shows that a desired task or trajectories can be still achieved even though there are feedback signal dropouts if the desired task is repetitive, as in the iterative learning control case. Specifically this paper shows how to design stochastic iterative learning control systems such that the networked-system with a repetitive task is robust stable against measurement and process noises and independent, intermittent output channel dropouts.

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Project ENSAYO: A virtual emergency operations center for disaster management research, training and discovery

Project ENSAYO: A virtual emergency operations center for di...

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2nd International Conference on Internet Monitoring and Protection, ICIMP 2007

作者： Becerra-Fernandez, Irma Prietula, Michael Madey, Greg Rodriguez, Domingo Florida International University Decision Sciences and Information Systems College of Business Administration Miami FL 33176 United States Emory University Decision and Information Analysis Goizueta Business School Atlanta GA 30322 United States University of Notre Dame Computer Science and Engineering College of Engineering Notre Dame IN 46556 United States University of Puerto Rico-Mayaguez Department of Electrical Engineering College of Engineering Mayaguez 00681 Puerto Rico

ISBN: (纸本)0769529119

Disaster management, before, during, and after the event, is dynamic, complex, and ill-defined, and the events themselves are rare and diverse due to the many characteristics that define them. Prior decisions may be thoroughly documented via after action reports, but these may not cover every issue as frequently unique and unanticipated events arise during each emergency. The nature of the decisions, where they are made, who makes them, the data and information resources required to make and monitor them, and the location of available knowledge to drive them may sometimes be unknown, unavailable, or both. This paper describes the research activities of Project Ensayo, which seeks to support and enable multiple research projects investigating processes, functions and structures present at Emergency Operations Centers, including situational awareness, knowledge management, inferences from dynamic data, disaster management and mechanisms of command, control, communication and coordination. © 2007 IEEE.

关键词： Virtual reality

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A Model-Based Feedback control Strategy for Heap Bioleaching Processes

A Model-Based Feedback Control Strategy for Heap Bioleaching...

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Proceedings of the 46th IEEE Conference on decision and control, Pages 3220-3677

作者： Boris I. Godoy Julio H. Braslavsky Juan C. Aguero School of Electrical Engineering & Computer Science University of Newcastle NSW Australia Centre for Complex Dynamic Systems & Control University of Newcastle NSW Australia

ISBN: (纸本)9781424414970;1424414970

In this paper we propose a feedback control strategy to increase the copper recovery in a heap bioleaching process. The control strategy is implemented using Internal Model control based on a multivariable linear state-space model obtained by the Maximum Likelihood method. The linear model describes the variations of the average temperature around a nominal trajectory. This trajectory is obtained as the response to nominal inputs that have been proved to work well in real leaching applications, and is computed using a comprehensive high complexity mathematical model developed by BHP Billiton Innovation. The results show that significant increments in copper extraction can be obtained using limited control actions with the proposed feedback strategy.

关键词： control strategy internal model control heap Copper extraction Bioleaching maximum likelihood feedback control Model control Nominal control action trajectory Billiton

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