Optimal measurement methods for distributed parameter system identification

  • 371 Pages
  • 1.97 MB
  • 7253 Downloads
  • English
by
CRC Press , Boca Raton, Fla
Distributed parameter systems, Mathematical optimization, System analysis, Control t
StatementDariusz Uciński
SeriesSystems and control series, Taylor & Francis systems and control book series
Classifications
LC ClassificationsQA402 .U35 2005
The Physical Object
Paginationxvii, 371 p. :
ID Numbers
Open LibraryOL17135530M
ISBN 100849323134
LC Control Number2004054473

Optimal Measurement Methods for Distributed Parameter System Identification discusses the characteristic features of the sensor placement problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, culminating in the most comprehensive and timely treatment of the issue by: 1st Edition Published on Septem by CRC Press For dynamic distributed systems modeled by partial differential equations, existing methods of sensor l Optimal Measurement Methods for Distributed Parameter System Identific.

Optimal Measurement Methods for Distributed Parameter System Identification Pages pages For dynamic distributed systems modeled by partial differential equations, existing methods of sensor location in parameter estimation experiments are either limited to one-dimensional spatial domains or require large investments in software by: Optimal measurement methods for distributed parameter system identification.

[Dariusz Uciński] -- "Unique in its focus, this book outlines optimal sensor placement strategies for parameter identification in dynamic distributed systems modeled by partial differential equations.

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Optimal Measurement Methods for Distributed Parameter System Identification discusses the characteristic features of the sensor placement problem. "Optimal Measurement Methods for Distributed Parameter System Identification" discusses the characteristic features of the sensor placement problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, culminating in the most comprehensive and timely treatment of the issue available.

Save on Optimal Measurement Methods for Distributed Parameter System Identification by Dariusz Ucinski. Shop your textbooks from Zookal AU today. For dynamic distributed systems modeled by partial differential equations, existing methods of sensor location in parameter estimation experiments are either limited to one-dimensional spatial domains.

The book covers topics of distributed parameter control systems in the areas of simulation, identification, state estimation, stability, control (optimal, stochastic, and coordinated), numerical approximation methods, optimal sensor, and actuator positioning. Abstract: The problem of optimal measurement locations for state estimation in linear distributed parameter systems is considered.

It has previously been shown that the optimal sensor location problem for distributed systems can be posed as an optimal control problem for a system described by the infinite-dimensional matrix Riccati equation for the filter covariance.

In control theory, a distributed parameter system (as opposed to a lumped parameter system) is a system whose state space is systems are therefore also known as infinite-dimensional systems.

Typical examples are systems described by partial differential equations or by delay differential equations. Optimal measurement methods for distributed parameter system identification.

[Dariusz Uciński] -- Ucinski (U. of Zielona G ra, Poland) offers an account of classical and recent work on sensor placement for parameter estimation in dynamic distributed systems modeled by partial differential.

Control of distributed parameter systems (DPS) remains a challenging task, as the system dynamics are infinite-dimensional.

Model reduction of such systems may produce instabilities and thus it is essential that the model reduction methodology used is robust. Different orthogonal functions are optimal for different parameter probability. As a matter of fact the control theory of distributed parameter systems covers a very large and diverse area of problems and methods and the study of some specific problems arising in applications represents much of the substance of the theory.

Optimal Design Techniques for Distributed Parameter Systems H.T. Banksy D. Rubioz N. Saintierx M.I. Troparevsky{Abstract A wide number of inverse problems consist in select-ing best parameter values of a given mathematical model based ts to measured data.

These are usu-ally formulated as optimization problems and the. Optimal sensor location for distributed parameter system identi cation (Part 1) Dariusz Ucinski Institute of Control and Computation Engineering Measurement accuracy: intro to optimal design The weights of objects A and B are to be measured using apan balanceand a set of standard weights.

Each weighing measures. investigated. In distributed parameter systems, besides the boundary perturbations, another important design variable is available, namely, the spatial location of measurement sensors.

A method to design optimal experiments for parameter estimation of a general distributed parameter system is. The book also ponders on stochastic differential equations in Hilbert space and their application to delay systems and linear quadratic optimal control problem over an infinite time horizon for a class of distributed parameter systems.

From those definitions we introduce the concepts of regional controllability and observability. Then, we describe the dynamics of the system in an appropriate way for the FIM framework of optimal sensor location for parameter estimation. We give the definitions of the parameter estimation and optimal.

Optimal measurement locations for Parameter Estimation of Distributed Parameter Systems based on the use of Artificial Neural Networks.

Alaña, J., Applied Soft Computing, Submitted, (). Optimal Spatial Sampling Scheme for Parameter Estimation of Non Linear Distributed Parameter Systems. Alaña, J. Identification of spatially varying parameters in distributed parameter systems from noisy data is an ill-posed problem.

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The concept of regularization, widely used in solving linear Fredholm integral equations, is developed for the identification of parameters in distributed parameter systems. Find helpful customer reviews and review ratings for Optimal Measurement Methods for Distributed Parameter System Identification (TAYLOR & FRANCIS SYSTEMS AND CONTROL BOOK SERIES.) at Read honest and unbiased product reviews from our users.5/5.

Lecture 12System Identification Prof. Munther A. Dahleh 2 • What do we know. We know methods for identifying “models” inside a “priori” given model structures. • How can we use this knowledge to provide a model for the plant, number of parameters, if the input is “exciting” only a.

Description Optimal measurement methods for distributed parameter system identification FB2

complete treatment of the whole subject of distributed parameter systems control. Inevitably, then, certain subjects are emphasized at the expense of others.

In this chapter, for example, we do not discuss at all the very important question of system identification in the distributed parameter context-a subject on which literally hundreds. where a 1 and a 2 are the model parameters. The model parameters are related to the system constants m, c, and k, and the sample time T s.

This difference equation shows the dynamic nature of the model. The displacement value at the time instant t depends not only on the value of force F at a previous time instant, but also on the displacement values at the previous two time instants y(t–1.

Methods of system identification, parameter estimation and optimisation applied to problems of modelling and control in engineering and physiology the development of inferential measurement. It was observed that this method depends on the magnitude of outputs values and report the measurement positions where the outputs reached their extrema values.

The D-optimal design method produces number and locations of the optimal measurements and it depends strongly of the sensitivity coefficients, but mostly of their behaviours.

Optimal Measurement Methods for Distributed Parameter System Identification. A Moving Horizon Approach to Networked Control System Design.

IEEE Transactions on Automatic Control, Vol. 49, Issue. 9, p. and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most.

Industrial Use of System ID • Process control - most developed ID approaches – all plants and processes are different – need to do identification, cannot spend too much time on each – industrial identification tools • Aerospace – white-box identification, specially designed programs of tests • Automotive.

the optimal control of distributed parameter systems is generally considered to have been initiated in 19 60 by Butkovskii and Lerner (1). The term "distributed parameter system" was coined by Butkovskii and was intended to refer to dynamical systems which are modeled by either partial dif­ ferential equations or by multiple integral equations.

SYSTEM IDENTIFICATION AND PARAMETER ESTIMATION (Professional Elective-V) COURSE CODE: 15EE L T P C Pre-requisites: Probability and statistics COURSE OUTCOMES: At the end of the course the student shall be able to CO 1 Understand the concepts of systems and models CO 2 Apply different model estimation methods for solving problems.

the optimal control of such systems has been to approximate the di­ stributed parameter systems with lumped parameter systems, which are governed by systems of ordinary differential equations, and to apply the well developed theory of optimal control for lumped parameter systems. In many instances approximating the distributed parameter.CONTROL SYSTEMS, ROBOTICS, AND AUTOMATION - Vol.

XIV - State Estimation in Distributed Parameter Systems - Vande Wouwer A. and Zeitz M. ©Encyclopedia of Life Support Systems (EOLSS) Owing to the infinite order of DPSs and the different classes of PDE models, care must be exercised in designing a Kalman filter or a Luenberger observer.

The fact that we observed the opposite in our study may be explained by several factors. First, considering the fact that the BACTEC system and the specialized media used by the system are more sensitive than conventional manual methods, it may be that, by using this system, bloodstream pathogens are detected in lower quantities in the blood.