Optimal Measurement Methods for Distributed Parameter System Identification: 1st Edition (Hardback) book cover

Optimal Measurement Methods for Distributed Parameter System Identification

1st Edition

By Dariusz Ucinski

CRC Press

392 pages | 51 B/W Illus.

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Hardback: 9780849323133
pub: 2004-08-27
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Description

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 systems. With the expense of scanning and moving sensors, optimal placement presents a critical 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. By presenting a step-by-step guide to theoretical aspects and to practical design methods, this book provides a sound understanding of sensor location techniques.

Both researchers and practitioners will find the case studies, the proposed algorithms, and the numerical examples to be invaluable. This text also offers results that translate easily to MATLAB and to Maple. Assuming only a basic familiarity with partial differential equations, vector spaces, and probability and statistics, and avoiding too many technicalities, this is a superb resource for researchers and practitioners in the fields of applied mathematics, electrical, civil, geotechnical, mechanical, chemical, and environmental engineering.

Table of Contents

INTRODUCTION

The Optimum Experimental Design Problem in Context

A General Overview of Literature

KEY IDEAS OF IDENTIFICATION AND EXPERIMENTAL DESIGN

System Description

Parameter Identification

Measurement Location Problem

Main Impediments

Deterministic Interpretation of the FIM

Calculation of Sensitivity Coefficients

A Final Introductory Note

LOCALLY OPTIMAL DESIGNS FOR STATIONARY SENSORS

Linear-in-Parameters Lumped Models

Construction of Minimax Designs

Continuous Designs in Measurement Optimization

Clusterization-Free Designs

Nonlinear Programming Approach

A Critical Note on Some Deterministic Approach

Modifications Required by Other Settings

Summary

LOCALLY OPTIMAL STRATEGIES FOR SCANNING AND MOVING OBSERVATIONS

Optimal Activation Policies for Scanning Sensors

Adapting the Idea of Continuous Designs for Moving Sensors

Optimization of Sensor Trajectories Based on Optimal-Control Techniques

Concluding Remarks

MEASUREMENT STRATEGIES WITH ALTERNATIVE DESIGN OBJECTIVES

Optimal Sensor Location for Prediction

Sensor Location for Model Discrimination

Conclusions

ROBUST DESIGNS FOR SENSOR LOCATION

Sequential Designs

Optimal Designs in the Average Sense

Optimal Designs in the Minimax Sense

Robust Sensor Location Using Randomized Algorithms

Concluding Remarks

TOWARDS EVEN MORE CHALLENGING PROBLEMS

Measurement Strategies in the Presence of Correlated Observations

Maximization of an Observability Measure

Summary

APPLICATIONS FROM ENGINEERING

Electrolytic Reactor

Calibration of Smog Prediction Models

Monitoring of Groundwater Resources Quality

Diffusion Process With Correlated Observational Errors

Vibrating H-Shaped Membrane

CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS

APPENDICES

List of Symbols

Mathematical Background

On Statistical Properties of Estimators

Analysis of the Largest Eigenvalue

Differentiation of Nonlinear Operators

Accessory Results for PDE's

Interpolation of Tabulated Sensitivity Coefficients

Differentials of Section 4.3.3

Solving Sensor Location Problems Using Maple and MATLAB

Subject Categories

BISAC Subject Codes/Headings:
MAT003000
MATHEMATICS / Applied
TEC007000
TECHNOLOGY & ENGINEERING / Electrical