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6th World Congress on Intelligent Control and Automation

作者： Li, Han-Xiong Zhang, Xian-Xia Li, Shao-Yuan City Univ Hong Kong Dept MEEM Hong Kong Hong Kong Peoples R China Shanghai Jiao Tong Univ Inst Automat Shanghai 200030 Peoples R China

ISBN: (纸本)1424403316

A three-dimensional fuzzy logic controller (3-D FLC) is presented for the control of distributed parameter system (DPS). Different to the traditional FLC, the 3-D FLC deals with three-dimensional fuzzy set (3-D fuzzy set), i.e. the traditional fuzzy set plus spatial dimension. The proposed FLC still consists of defuzzification, rule inference, and defuzzilication. Different to the traditional FLC, the 3-D FLC can fuse information measured from space domain into a spatial membership function, and then rules will not increase as sensors increase for spatial measurement. Therefore, the 3-D FLC has the capability to handle spatial information. Application of the 3-D FLC is presented for a catalytic packed-bed reactor to show the effectiveness of the new fuzzy controller design, and comparisons with the traditional fuzzy controller are also given.

关键词： fuzzy set fuzzy logic controller distributed parameter system three-dimensional fuzzy set

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Profile control in distributed parameter systems using lexicographic optimization based MPC

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JOURNAL OF PROCESS CONTROL 2009年第1期19卷 100-109页

作者： Padhiyar, Nitin Bhartiya, Sharad Indian Inst Technol Dept Chem Engn Bombay 400076 Powai India

Process equipment that exhibits significant spatial variation of system properties, such as temperature or concentration in a fixed bed reactor, are typically modeled as distributed parameter systems. While some properties of the final product exiting the equipment may depend oil the states concerning the endpoint, others may be a function of the history of processing within the equipment. In such instances, control of the spatial property profile may be beneficial. In this work, we explore the idea of profile control using extended MPC and outline the additional challenges that must be addressed in this context. In case that the target profile is unachievable, we present an MPC formulation that uses lexicographic optimization to prioritize the different sections of the profile. Simulation of a simple representative system namely a hypothetical plug flow reactor is used to demonstrate that the lexicographic optimization based M PC provides a systematic approach to profile control and spans between the endpoint control strategy and the whole profile control strategy. The benefits of lexicographic optimization based MPC were also demonstrated oil a large-scale distributed parameter system of industrial size, namely the continuous pulp digester. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： distributed parameter system Extended Kalman filter Model-based control Continuous pulp digester Lexicographic optimization

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ON APPLICABILITY OF MATHEMATICAL MODELS BASED ON FRACTIONAL CALCULUS TO SHIP DYNAMICS

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IFAC Proceedings Volumes 2010年第20期43卷 190-195页

作者： Serge Sutulo C. Guedes Soares Centre for Marine Technology and Engineering (CENTEC) Technical University of Lisbon Instituto Superior Técnico Av. Rovisco Pais 1049-001 Lisbon Portugal

Perspectives of application of mathematical models using concepts of fractional derivatives and integrals in ship dynamics are analyzed under the assumption that these must approximate the well-known and generally acknowledged model described by a set of integro-differential equations. Possible equivalence is discussed mainly on the basis of the Fourier image in the frequency domain. It is demonstrated that advantages of invoking extended calculus techniques are far from being evident and it seems premature to declare such models as very promising in ship dynamics and control applications. Copyright ©2010 IFAC

关键词： Mathematical models ship dynamics integro-differential equations fractional differential equations distributed parameter system Fourier transforms frequency response analytic approximation

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Configuration of sensor network with uncertain location of nodes for parameter estimation in distributed parameter systems. *

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IFAC Proceedings Volumes 2009年第13期42卷 31-36页

作者： Maciej Patan Dariusz Uciński Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 65-246 Zielona Góra Poland

Abstract A sensor location problem for monitoring network with stationary nodes used for estimating unknown parameters of distributed-parameter system is addressed. In particular, the situation is considered, when the actual spatial positions of sensor nodes at the experimentation stage may be uncertain to some extent and randomly fluctuate around some locations specified at the configuration stage. In the presented approach, some results from experimental design theory for dynamic systems with random regressors are extended for the purpose of configuring a sensor network. Then, a simple algorithm based on the notion of approximate near minimum from statistical learning theory is adapted to select the most informative sensor locations. The delineated approach is illustrated by numerical example on a sensor network design for a two-dimensional convective diffusion process.

关键词： sensor network distributed parameter system parameter estimation

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A time/space separation-based Hammerstein modeling approach for nonlinear distributed parameter processes

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COMPUTERS & CHEMICAL ENGINEERING 2009年第7期33卷 1247-1260页

作者： Qi, Chenkun Li, Han-Xiong City Univ Hong Kong Dept Management Eng & Eng Management Kowloon Hong Kong Peoples R China

Modeling of distributed parameter systems (DPSs) is very difficult because of their infinite-dimensional, spatio-temporal nature and nonlinearities. A low-order, simple nonlinear and parsimonious model is often required in real applications. In this study, a time/space separation-based Hammerstein modeling approach is proposed for unknown nonlinear DPS. Firstly, the Karhunen-Loeve (KL) method is used for the time/space separation, where the spatio-temporal output is decomposed into a few dominant spatial basis functions with temporal coefficients. Secondly, a low-order parsimonious Hammerstein model is identified from the low-dimensional data to reconstruct the system dynamics, where the parsimonious model structure is determined by the orthogonal forward regression and the parameters are estimated using the least squares estimation and the singular value decomposition. The algorithm does not require nonlinear optimization and it is numerically robust. This modeling is very suitable for control design. The simulations are presented to show the effectiveness of the proposed modeling method. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： distributed parameter system system identification Hammerstein model Karhunen-Loeve decomposition Orthogonal forward regression

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The strong stabilization of a one-dimensional wave equation by non-collocated dynamic boundary feedback control

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AUTOMATICA 2009年第3期45卷 790-797页

作者： Guo, Bao-Zhu Guo, Wei Acad Sinica Acad Math & Syst Sci Beijing 100190 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Univ Witwatersrand Sch Computat & Appl Math ZA-2050 Wits Johannesburg South Africa

The stabilization of a one-dimensional wave equation with non-collocated observation at its unstable free end and control at another end is considered. The controller comprises a state estimator which is designed in the case where the velocity is not available. The method of "backstepping" is adopted in our design of the feedback law. We use the theory of Co-semigroups and Lyapunov functionals to prove the strong stability of the resulting closed-loop system. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Vibrating string distributed parameter system Boundary control Backstepping

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Two state estimators for the barium sulfate precipitation in a semi-batch reactor

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CHEMICAL ENGINEERING SCIENCE 2009年第4期64卷 646-660页

作者： Mangold, M. Bueck, A. Schenkendorf, R. Steyer, C. Voigt, A. Sundmacher, K. Max Planck Inst Dynam Komplexer Tech Syst D-39106 Magdeburg Germany Otto VonGuericke Univ Magdegurg Lehrstuhl Systemverfahrenstech D-39106 Magdeburg Germany

The on-line determination of particle property distributions by direct measurements is often difficult, because the measurement equations are not invertible or because the inverse problem is ill-posed. If the process is observable, one can use state estimation techniques in order to reconstruct unmeasurable internal states of the process. This is discussed here for a semi-batch precipitation reactor. A square root unscented Kalman filter and state estimation by online minimisation are studied for the case of a measurable average particle size. Both estimators use a one-dimensional population balance model. The two approaches are compared in simulations. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Precipitation Population balance State estimation Observer distributed parameter system Unscented Kalman filter Observation by online minimisation

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A multi-channel spatio-temporal Hammerstein modeling approach for nonlinear distributed parameter processes

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JOURNAL OF PROCESS CONTROL 2009年第1期19卷 85-99页

作者： Qi, Chenkun Zhang, Hai-Tao Li, Han-Xiong City Univ Hong Kong Dept Mfg Engn & Engn Management Kowloon Hong Kong Peoples R China Huazhong Univ Sci & Technol Dept Control Sci & Engn Wuhan 430074 Peoples R China Univ Cambridge Dept Engn Cambridge CB1 1PZ England

Modeling of distributed parameter processes is a challenging problem because of their complex spatio-temporal nature, nonlinearities and uncertainties. In this study, a spatio-temporal Hammerstein modeling approach is proposed for nonlinear distributed parameter processes. Firstly, the static nonlinear and the distributed dynamical linear parts of the Hammerstein model are expanded onto a set of spatial and temporal basis functions. In order to reduce the parametric complexity, the Karhunen-Loeve decomposition is used to find the dominant spatial bases with Laguerre polynomials selected as the temporal bases. Then, using the Galerkin method, the spatio-temporal modeling will be reduced to a traditional temporal modeling problem. Finally, the unknown parameters can be easily estimated using the least squares estimation and the singular value decomposition. In the presence of unmodeled dynamics, a multi-channel modeling framework is proposed to further improve the modeling performance. The convergence of the modeling can be guaranteed under certain conditions. The simulations are presented to show the effectiveness of this modeling method and its potential to a wide range of distributed processes. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： distributed parameter system Spatio-temporal modeling Hammerstein model Galerkin method Karhunen-Loeve decomposition Laguerre polynomial

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Microvia fill process control

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INTERNATIONAL JOURNAL OF CONTROL 2009年第5期82卷 883-893页

作者： Tenno, R. Pohjoranta, A. Aalto Univ Dept Automat & Syst Technol FIN-02150 Espoo Finland

In this article, the via fill ratio is stabilised at a desired level using optimal control values of plating time and current density for the plating process galvanostat. Both control variables are evaluated as functions of the process state, and operating conditions follow typical preferences for the via fill technology applied in manufacturing of multilayered printed circuit boards. The optimal controls are obtained as a system of two equations solved numerically with the gradient descent method. Results of the numerical analysis are presented and discussed.

关键词： distributed parameter system process control optimisation

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Nonlinear dimension reduction based neural modeling for distributed parameter processes

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CHEMICAL ENGINEERING SCIENCE 2009年第19期64卷 4164-4170页

作者： Qi, Chenkun Li, Han-Xiong City Univ Hong Kong Dept Mfg Engn & Engn Management Kowloon Hong Kong Peoples R China

Many chemical processes are nonlinear distributed parameter systems with unknown uncertainties. For this class of infinite-dimensional systems, the low-order model identification from process data is very important in practice. The dimension reduction with a principal component analysis (PCA) is only a linear approximation for nonlinear problem. In this study, a nonlinear dimension reduction based low-order neural model identification approach is proposed for nonlinear distributed parameter processes. First, a nonlinear principal component analysis (NL-PCA) network is designed for the nonlinear dimension reduction, which can transform the high-dimensional spatio-temporal data into a low-dimensional time domain. Then, a neural system can be easily identified to model this low-dimensional temporal data. Finally, the spatio-temporal dynamics can be reproduced using the nonlinear time/space reconstruction. The simulations on a typical nonlinear transport-reaction process show that the proposed approach can achieve a better performance than the linear PCA based modeling approach. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： distributed parameter system Low-order modeling Dimension reduction Nonlinear principal component analysis Neural network Transport-reaction process

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