Nonlinear estimation and applications to industrial systems control

Nonlinear estimation and applications to industrial systems control / Gerasimos Rigatos, editor.

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Bibliographic Details
Other Authors: Rigatos, Gerasimos G., 1971-
Format: eBook
Language:English
Published: New York : Nova Science Publishers, [2012]
Series:Engineering tools, techniques and tables.
Mathematics research developments series.
Subjects:
Automatic control.
Nonlinear theories.
Estimation theory.
TECHNOLOGY & ENGINEERING > Automation.
TECHNOLOGY & ENGINEERING > Robotics.
Automatic control
Estimation theory
Nonlinear theories
Online Access:Click for online access
Table of Contents:
  • NONLINEAR ESTIMATION AND APPLICATIONS TO INDUSTRIAL SYSTEMS CONTROL; NONLINEAR ESTIMATION AND APPLICATIONS TO INDUSTRIAL SYSTEMS CONTROL; LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA; CONTENTS; PREFACE; Chapter 1: A GENERALIZED ROBUST FILTERING FRAMEWORK FOR NONLINEAR DIFFERENTIAL-ALGEBRAIC SYSTEMS WITH UNCERTAINTIES; Abstract; 1. Introduction; 2. Preliminaries and Problem Statement; 2.1. Filter Structure; 2.2. Disturbance Attenuation Level; 3. H1 Filter Synthesis; 4. Converting SDP into Strict LMIs; 5. Robustness Against Nonlinear Uncertainty; 6. Illustrative Example.
  • 7. Conclusions and Future Research DirectionsReferences; Chapter 2: VARIANCE-CONSTRAINED FILTERING FOR A CLASS OF NONLINEAR STOCHASTIC SYSTEMS; Abstract; 1. Introduction; 2. Filtering Problem for Time-Invariant Systems; 2.1. Problem Formulation; 2.2. Stability and Variance Analysis; 2.3. Robust Filter Design; future. 2.4.esrmns; 3. Filtering Problem with Missing Measurements ; 3.1. Problem Formulation; 3.2. Stability and Variance Analysis; 3.3. Robust Filter Design with Measurements Missing; 3.4. Robust Filter Design with Multiple Measurements Missing; 3.5. Numerical Example; 3.6. Summary.
  • 4. Filtering Problem for Time-Varying Systems4.1. Problem Formulation; 4.2. System Covariance Analysis; 4.3. Robust Filter Design; 4.4. Numerical Example; 4.5. Summary; References; Chapter 3: RANDOM COEFFICIENT MATRICES KALMAN FILTERING WITH APPLICATIONS; Abstract; 1. Introduction; 2. Random Coefficient Matrices Kalman Filtering; 2.1. Estimator of the Random Coefficient Matrices Dynamic System; 2.2. Optimal Distributed Random Coefficient Matrices Kalman Filtering Fusion; 2.3. Numerical Examples; 3. Application to Multi-Target Tracking; 3.1. Background; 3.2. Single-Sensor DAIRKF.
  • 3.3. Multisensor DAIRKF3.4. Numerical Examples; 4. Conclusion; Appendix; Acknowledgments; References; Chapter 4: ONLINE DISTRIBUTED EVALUATION OF INTERDEPENDENT CRITICAL INFRASTRUCTURES; Abstract; 1. Introduction; 2. Interdependency Modeling: State of the Art; 3. Mixed Holistic-Reductionistic Model; 3.1. Critical Infrastructure Simulation by Interdependent Agent (CISIA); 4. MICIE Online System; 5. Consensus; 6. Consensus of Fuzzy Variables; 6.1. Fuzzy Variables and Systems; 6.2. Fuzzy Consensus; 7. Case Study; 7.1. Power Grid; 7.2. SCADA Network; 7.3. Telecommunication Network.
  • 7.4. An Illustrative Example8. Conclusions; Acknowledgement; References; Chapter 5: NONLINEAR ESTIMATION AND FAULT DETECTION IN LARGE-SCALE INDUSTRIAL HVAC SYSTEMS; Abstract; 1. Introduction; Motivation; Previous Work; Complications; Overview; 2. HVAC Systems; Architecture; Modes; Dynamic Modes; Static Modes; Combination Modes; Failures; Classification Based on Effect; Classification Based on Onset; Model Structure; 3. Mathematical Model & Examples; Multi-Unit Hybrid System Model; Assumptions; System Model Examples; 4. General Approach; Remarks; 5. Algorithm Description; FD Algorithm.

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