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

Format:

• Book

Contributor:

• Rigatos, Gerasimos G., 1971-

Series:

• Engineering tools, techniques and tables.
• Mathematics research developments series.
• Engineering tools, techniques and tables
• Mathematics research developments

Language:

English

Subjects (All):

• Automatic control.
• Nonlinear theories.
• Estimation theory.

Physical Description:

1 online resource (379 p.)

Edition:

1st ed.

Place of Publication:

Hauppauge, N.Y. : Nova Science Publishers, Inc., [2012]

Language Note:

English

Summary:

This text re-examines conservatism, the dominant ideology, politics, and discourse in America since the 1980s through the 2010s. The book reveals medievalism, including feudalism, despotism, and medieval theocracy, as the point of origin, 'golden past' of conservatism.

Contents:

• Intro
• 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 Directions
• References
• Chapter 2: VARIANCE-CONSTRAINED FILTERING FOR A CLASS OF NONLINEAR STOCHASTIC SYSTEMS
• 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 Systems
• 4.1. Problem Formulation
• 4.2. System Covariance Analysis
• 4.3. Robust Filter Design
• 4.4. Numerical Example
• 4.5. Summary
• Chapter 3: RANDOM COEFFICIENT MATRICES KALMAN FILTERING WITH APPLICATIONS
• 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 DAIRKF
• 3.4. Numerical Examples
• 4. Conclusion
• Appendix
• Acknowledgments
• Chapter 4: ONLINE DISTRIBUTED EVALUATION OF INTERDEPENDENT CRITICAL INFRASTRUCTURES
• 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 Example
• 8. Conclusions
• Acknowledgement
• Chapter 5: NONLINEAR ESTIMATION AND FAULT DETECTION IN LARGE-SCALE INDUSTRIAL HVAC SYSTEMS
• 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
• Implementation Details
• Estimation (Filt)
• Mode Change Detection (Detect)
• Reinitialization (Reinit)
• Fault Detection
• Unit Exclusion
• Missing Measurements
• Threshold Selection
• Limitations
• Advantages
• 6. Performance Evaluation
• Simulation Results
• Mode Estimation/Identification
• Real Data
• Sources
• Data Collection Issues
• Results
• 7. Summary
• Appendix: ExpectationMaximization for Multiple Mixtures
• Multiple-Mixture Model
• Derivation of MMEM
• References.
• Chapter 6: NONLINEAR OBSERVERS FOR ESTIMATION OF STATE AND KINETICS IN BIOPROCESSES
• 2. Non-Linear Dynamical Models of Bioprocesses
• 2.1. The General Dynamical State-Space Model of Bioprocesses
• 2.2. Dynamical Model of an Anaerobic Wastewater Biodegradation Process
• 2.3. Dynamical Model of an Activated Sludge Process
• 3. Asymptotic State Observers
• 3.1. General Form of the State Observer
• 3.2. Asymptotic Observers for State Estimation when the Reaction Rates Are Unknown
• 3.2.1. General Structure of Asymptotic Observer for State Estimation
• 3.2.2. A Simplified Structure of State Observers
• 3.2.3. Stability of the Asymptotic State Observer
• 4. Parameters Estimators
• 4.1. Identification of the Yield Coefficients
• 4.2. On-Line Estimation of Reaction Rates
• 4.2.1 Problem Statement
• 4.2.2. An Observer-Based Parameter Estimator
• 4.2.3. A Linear Regression Parameter Estimator
• 4.3. Joint Estimation of Yield Coefficients and Specific Reaction Rates
• 4.3.1. Full State Measurement
• 4.3.2. Partial State Measurement
• 5. Adaptive Control of Wastewater Treatment Bioprocesses
• 5.1. Control Strategies of an Anaerobic Wastewater Biodegradation Process
• 5.1.1. Problem Statement
• 5.1.2. Exactly Linearizing Feedback Control
• 5.1.3. Adaptive Control Strategies
• 5.1.4. Simulations Result
• 5.2. Control Strategies of an Activated Sludge Process
• 5.2.1. Problem Statement
• 5.2.2. Adaptive Control of an Activated Sludge Process
• 5.2.3. Simulation Results
• 6. Conclusions
• Annexes
• Annex A. State Transformations
• Annex B. Proof of Theorum 1
• Annex C. Proof of Lemma 1
• Annex D. Stability and Convergence of Linear Regression Parameter Estimator
• Acknowlegment
• Chapter 7: REVIEW OF NONLINEAR KALMAN, ENSEMBLE AND PARTICLE FILTERING WITH APPLICATION TO THE RESERVOIR HISTORY MATCHING PROBLEM
• 2. Recursive Bayesian Estimation and the Optimal Nonlinear Filter
• 3. The Kalman Filter and its Variants
• 3.1. The Kalman and Information Filters in Linear/Gaussian Systems
• 3.2. Nonlinear Kalman Filters
• 3.3. The Ensemble Kalman Filter
• 3.3.1. The Stochastic Ensemble Kalman Filter (SEnKF)
• 3.3.2. The Singular Evolutive Interpolated Kalman Filter (SEIK)
• 4. Nonlinear Non-Gaussian Bayesian Filters
• 4.1. The Particle Filter
• 4.2. The Gaussian Sum Filter
• 4.3. Convergence and Practical Issues
• 5. Application to History Matching in a Reservoir Model
• 5.1. Model Description
• 5.2. Experiment Settings
• 5.3. Experiment Results
• 6. Discussion and Conclusion
• Chapter 8: OBSERVER-BASED INDIRECT ADAPTIVE SLIDING MODE CONTROL DESIGN AND IMPLEMENTATION FOR A CLASS OF NONLINEAR SYSTEMS
• 2. Type-2 Fuzzy Logic System
• 3. Problem Statement
• 4. Proposed Approach
• 5. Experimental Results
• 6. Conclusion and FutureWork
• Chapter 9: CONSENSUS-BASED PARTICLE FILTER IMPLEMENTATIONS FOR DISTRIBUTED NON-LINEAR SYSTEMS
• 1.1. Literature Review
• 1.2. Summary of The Contributions
• 2. The Particle Filter
• 2.1. The Centralized Implementation
• 2.2. Distributed Configuration
• 3. Average Consensus Algorithms
• 3.1. Discrete Time Linear Consensus Algorithms
• 4. The GLC/DPF Implementation
• 5. The UCD/DPF ImplementationIn
• 5.1. Centralized Unscented Particle Filter
• 5.2. Consensus-Based Distributed UPF
• 6. The GCF/DPF Implementation
• 6.1. Consensus Algorithms
• 7. Computational Complexity
• 8. Simulation: Bearing Only Target Tracking (BOT).
• 8.1. Scenario 1
• 8.2. Scenario 2
• 9. Conclusion
• A. Details of the Computational Complexity
• Chapter 10: ENVIRONMENTAL FUNCTION MODELING WITH SPARSE GAUSSIAN PROCESS REGRESSION
• 2. Gaussian Process Regression
• 2.1. Gaussian Process Regression
• 2.2. Sparse Gaussian Process Regression
• 3. Distributed Gaussian Process Regression
• 3.1. Compactly Supported Covariance Functions
• 3.2. Distributed Gaussian Process Regression
• 3.3. Hyperparameter Learning
• 4. Simulations
• 5. Conclusions
• Chapter 11: DIFFERENTIAL FLATNESS THEORY AND EXTENDED KALMAN FILTERING FOR SENSORLESS CONTROL OF DOUBLY-FED INDUCTION GENERATORS
• 2. Model of the Doubly-Fed Induction Generator
• 2.1. The Complete Sixth-order Model of the Induction Generator
• 2.2. Simplified Low-order Power Generator Models
• 2.2.1. The Third-order DFIG Model
• 2.2.2. The Synchronous Generator Model
• 3. Differential Flatness of the Doubly-Fed Induction Generator
• 3.1. Properties of Differentially Flat Systems
• 3.2. Differential Flatness for the DFIG Model
• 4. Control of the Doubly-Fed Induction Generator
• 5. Flux and Rotation Speed Estimator
• 5.1. Implementation of the EKF for Sensorless Control of the DFIG
• 5.2. Estimation of theWind-generated Mechanical Torque Using EKF
• 6. Simulation Tests
• 7. Conclusions
• Chapter 12: FLATNESS-BASED ADAPTIVE FUZZY CONTROL FOR MIMO NONLINEAR DYNAMICAL SYSTEMS
• 2. Differential Flatness for Nonlinear Dynamical Systems
• 2.1. Definition of Differentially Flat Systems
• 2.2. Differentially Flat Systems The following classes
• 2.3. Conditions for Applying the Differential Flatness Theory
• 3. Flatness-based Adaptive Fuzzy Control for MIMO Nonlinear Systems.
• 3.1. Transformation of MIMO Nonlinear Systems into the Brunovsky Form.

Notes:

• Description based upon print version of record.
• Includes bibliographical references and index.
• Description based on print version record.

ISBN:

1-62257-260-2

OCLC:

831664479