Analysis and synthesis of fault-tolerant control systems /

In recent years, control systems have become more sophisticated in order to meet increased performance and safety requirements for modern technological systems. Engineers are becoming more aware that conventional feedback control design for a complex system may result in unsatisfactory performance,...

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Bibliographic Details
Main Author: Mahmoud, Magdi S.
Other Authors: Xia, Yuanqing.
Format: eBook
Language:English
Published: Chichester, West Sussex : Wiley, [2013]
Subjects:
Online Access:CONNECT
CONNECT
Table of Contents:
  • Machine generated contents note: 1. Introduction
  • 1.1. Overview
  • 1.2. Basic Concepts of Faults
  • 1.3. Classification of Fault Detection Methods
  • 1.3.1. Hardware redundancy based fault detection
  • 1.3.2. Plausibility test
  • 1.3.3. Signal-based fault diagnosis
  • 1.3.4. Model-based fault detection
  • 1.4. Types of Fault-Tolerant Control System
  • 1.5. Objectives and Structure of AFTCS
  • 1.6. Classification of Reconfigurable Control Methods
  • 1.6.1. Classification based on control algorithms
  • 1.6.2. Classification based on field of application
  • 1.7. Outline of the Book
  • 1.7.1. Methodology
  • 1.7.2. Chapter organization
  • 1.8. Notes
  • References
  • 2. Fault Diagnosis and Detection
  • 2.1. Introduction
  • 2.2. Related Work
  • 2.2.7. Model-based schemes
  • 2.2.2. Model-free schemes
  • 2.2.3. Probabilistic schemes
  • 2.3. Integrated Approach
  • 2.3.1. Improved multi-sensor data fusion
  • 2.3.2. Unscented transformation
  • 2.3.3. Unscented Kalman filter
  • 2.3.4. Parameter estimation
  • 2.3.5. Multi-sensor integration architectures
  • 2.4. Robust Unscented Kalman Filter
  • 2.4.1. Introduction
  • 2.4.2. Problem formulation
  • 2.4.3. Residual generation
  • 2.4.4. Residual evaluation
  • 2.5. Quadruple Tank System
  • 2.5.7. Model of the QTS
  • 2.5.2. Fault scenarios in QTS
  • 2.5.3. Implementation structure of UKF
  • 2.5.4. UKF with centralized multi-sensor data fusion
  • 2.5.5. UKF with decentralized multi-sensor data fusion
  • 2.5.6. Drift detection
  • 2.6. Industrial Utility Boiler
  • 2.6.7. Steam flow dynamics
  • 2.6.2. Drum pressure dynamics
  • 2.6.3. Drum level dynamics
  • 2.6.4. Steam temperature
  • 2.6.5. Fault model for the utility boiler
  • 2.6.6. Fault scenarios in the utility boiler
  • 2.6.7. UKF with centralized multi-sensor data fusion
  • 2.6.8. UKF with decentralized multi-sensor data fusion
  • 2.6.9. Drift detection
  • 2.6.70. Remarks
  • 2.7. Notes
  • References
  • 3. Robust Fault Detection
  • 3.1. Distributed Fault Diagnosis
  • 3.1.1. Introduction
  • 5.1.2. System model
  • 3.1.3. Distributed FDI architecture
  • 3.1.4. Distributed fault detection method
  • 5.1.5. Adaptive thresholds
  • 5.1.6. Distributed fault isolation method
  • 5.1.7. Adaptive thresholds for DFDI
  • 3.1.8. Fault detectability condition
  • 3.1.9. Fault isolability analysis
  • 3.1.10. Stability and learning capability
  • 3.2. Robust Fault Detection Filters
  • 5.2.1. Reference model
  • 5.2.2. Design of adaptive threshold
  • 5.2.3. Iterative update of noise mean and covariance
  • 3.2.4. Unscented transformation (UT)
  • 5.2.5. Car-like mobile robot application
  • 3.3. Simultaneous Fault Detection and Control
  • 3.3.1. Introduction
  • 3.3.2. System model
  • 3.3.3. Problem formulation
  • 3.3.4. Simultaneous fault detection and control problem
  • 3.3.5. Two-tank system simulation
  • 3.4. Data-Driven Fault Detection Design
  • 3.4.1. Introduction
  • 3.4.2. Problem formulation
  • 3.4.3. Selection of weighting matrix
  • 3.4.4. Design of FDF for time-delay system
  • 3.4.5. LMI design approach
  • 3.4.6. Four-tank system simulation
  • 3.5. Robust Adaptive Fault Estimation
  • 3.5.1. Introduction
  • 3.5.2. Problem statement
  • 3.5.3. Adaptive observer
  • 3.6. Notes
  • References
  • 4. Fault-Tolerant Control Systems
  • 4.1. Model Prediction-Based Design Approach
  • 4.1.1. Introduction
  • 4.1.2. System description
  • 4.1.3. Discrete-time UKF
  • 4.1.4. Unscented Transformation (UT)
  • 4.1.5. Controller reconfiguration
  • 4.1.6. Model predictive control
  • 4.1.7. Interconnected CSTR units
  • 4.1.8. Four-tank system
  • 4.1.9. Simulation results
  • 4.1.10. Drift detection in the interconnected CSTRs
  • 4.1.11. Information fusion from UKF
  • 4.1.12. Drift detection in the four-tank system
  • 4.2. Observer-Based Active Structures
  • 4.2.1. Problem statement
  • 4.2.2. separation principle
  • 4.2.3. FDI residuals
  • 4.2.4. Control of integrity
  • 4.2.5. Overall stability
  • 4.2.6. Design outline
  • 4.2.7. Design of an active FTC scheme
  • 4.2.8. Extraction of FDI-FTC pairs
  • 4.2.9. Simulation
  • 4.3. Notes
  • References
  • 5. Fault-Tolerant Nonlinear Control Systems
  • 5.1. Comparison of Fault Detection Schemes
  • 5.2. Fault Detection in Nonlinear Systems
  • 5.3. Nonlinear Observer-Based Residual Generation Schemes
  • 5.3.1. General considerations
  • 5.3.2. Extended Luenberger observer
  • 5.3.3. Nonlinear identity observer approach
  • 5.3.4. Unknown input observer approach
  • 5.3.5. disturbance decoupling nonlinear observer approach
  • 5.3.6. Adaptive nonlinear observer approach
  • 5.3.7. High-gain observer approach
  • 5.3.8. Sliding-mode observer approach
  • 5.3.9. Geometric approach
  • 5.3.10. Game-theoretic approach
  • 5.3.11. Observers for Lipschitz nonlinear systems
  • 5.3.12. Lyapunov-reconstruction-based passive scheme
  • 5.3.13. Time-varying results
  • 5.3.14. Optimization-based active scheme
  • 5.3.15. Learning-based active scheme
  • 5.3.16. Adaptive backstepping-based active scheme
  • 5.3.17. Switched control-based active scheme
  • 5.3.18. Predictive control-based active scheme
  • 5.4. Integrated Control Reconfiguration Scheme
  • 5.4.1. Introduction
  • 5.4.2. Basic features
  • 5.4.3. Nonlinear model of a pendulum on a cart
  • 5.4.4. NGA adaptive filter design
  • 5.4.5. Simulation results
  • 5.4.6. Performance evaluation
  • 5.4.7. Comparative studies
  • 5.5. Notes
  • References
  • 6. Robust Fault Estimation
  • 6.1. Introduction
  • 6.2. System Description
  • 6.3. Multiconstrained Fault Estimation
  • 6.3.1. Observer design
  • 6.3.2. Existence conditions
  • 6.3.3. Improved results
  • 6.3.4. Simulation results
  • 6.4. Adaptive Fault Estimation
  • 6.4.7. Introduction
  • 6.4.2. Problem statement
  • 6.4.3. Robust adaptive estimation
  • 6.4.4. Internal stability analysis
  • 6.4.5. Robust performance index
  • 6.4.6. Simulation
  • 6.5. Adaptive Tracking Control Scheme
  • 6.5.1. Attitude dynamics
  • 6.5.2. Fault detection scheme
  • 6.5.3. Fault-tolerant tracking scheme
  • 6.6. Notes
  • References
  • 7. Fault Detection of Networked Control Systems
  • 7.1. Introduction
  • 7.2. Problem Formulation
  • 7.3. Modified Residual Generator Scheme
  • 7.3.1. Modified residual generator and dynamic analysis
  • 7.3.2. Residual evaluation
  • 7.3.3. Co-design of residual generator and evaluation
  • 7.4. Quantized Fault-Tolerant Control
  • 7.4.1. Introduction
  • 7.4.2. Problem statement
  • 7.4.3. Quantized control design
  • 7.4.4. Simulation
  • 7.5. Sliding-Mode Observer
  • 7.5.1. Introduction
  • 7.5.2. Dynamic model
  • 7.5.3. Limited state measurements
  • 7.5.4. Simulation results: full state measurements
  • 7.5.5. Simulation results: partial state measurements
  • 7.6. Control of Linear Switched Systems
  • 7.6.1. Introduction
  • 7.6.2. Problem formulation
  • 7.6.3. Stability of a closed-loop system
  • 7.6.4. Simulation
  • 7.7. Notes
  • References
  • 8. Industrial Fault-Tolerant Architectures
  • 8.1. Introduction
  • 8.2. System Architecture
  • 8.3. Architecture of a Fault-Tolerant Node
  • 8.3.1. Basic architecture
  • 8.3.2. Architecture with improved reliability
  • 8.3.3. Symmetric node architecture
  • 8.3.4. Results
  • 8.4. Recovery Points
  • 8.5. Networks
  • 8.6. System Fault Injection and Monitoring
  • 8.6.1. Monitoring systems
  • 8.6.2. Design methodology
  • 8.7. Notes
  • References
  • 9. Fault Estimation for Stochastic Systems
  • 9.1. Introduction
  • 9.2. Actuator Fault Diagnosis Design
  • 9.3. Fault-Tolerant Controller Design
  • 9.4. Extension to an Unknown Input Case
  • 9.5. Aircraft Application
  • 9.5.1. Transforming the system into standard form
  • 9.5.2. Simulation results
  • 9.6. Router Fault Accommodation in Real Time
  • 9.6.1. Canonical controller and achievable behavior
  • 9.6.2. Router modeling and desired behavior
  • 9.6.3. Description of fault behavior
  • 9.6.4. least restrictive controller
  • 9.7. Fault Detection for Markov Jump Systems
  • 9.7.1. Introduction
  • 9.7.2. Problem formulation
  • 9.7.3. H∞ bounded real lemmas
  • 9.7.4. H∞ FD filter design
  • 9.7.5. Simulation
  • 9.8. Notes
  • References
  • -- 10. Applications
  • 10.1. Detection of Abrupt Changes in an Electrocardiogram
  • 10.1.1. Introduction
  • 10.1.2. Modeling ECG signals with an AR model
  • 10.1.3. Linear models with additive abrupt changes
  • 10.1.4. Off-line detection of abrupt changes in ECG
  • 10.1.5. Online detection of abrupt changes in ECG
  • 10.2. Detection of Abrupt Changes in the Frequency Domain
  • 10.2.1. Introduction
  • 10.2.2. Problem formulation
  • 10.2.3. Frequency domain ML ratio estimation
  • 10.2.4. Likelihood of the hypothesis of no abrupt change
  • 10.2.5. Effect of an abrupt change
  • 10.2.6. Simulation results
  • 10.3. Electromechanical Positioning System
  • 10.3.1. Introduction
  • 10.3.2. Problem formulation
  • 10.3.3. Test bed
  • 10.4. Application to Fermentation Processes
  • 10.4.1. Nonlinear faulty dynamic system
  • 10.4.2. Residual characteristics
  • 10.4.3. parameter filter
  • 10.4.4. Fault filter
  • 10.4.5. Fault isolation and identification
  • 10.4.6. Isolation speed
  • 10.4.7. Parameter partition
  • 10.4.8. Adaptive intervals
  • 10.4.9. Simulation studies
  • 10.5. Flexible-Joint Robots
  • 10.5.1. Problem formulation
  • 10.5.2. Fault detection scheme
  • Note continued: 10.5.3. Adaptive fault accommodation control
  • 10.5.4. Control with prescribed performance bounds
  • 10.5.5. Simulation results
  • 10.6. Notes
  • References
  • A. Supplementary Information
  • A.1. Notation
  • A. 1.1 Kronecker products
  • A.7.2. Some definitions
  • A.1.3. Matrix lemmas
  • A.2. Results from Probability Theory
  • A.2.1. Results-A
  • A.2.2. Results-B
  • A.2.3. Results-C
  • A.2.4. Minimum mean square estimate
  • A.3. Stability Notions
  • A.3.1. Practical stabilizability
  • A.3.2. Razumikhin stability
  • A.4. Basic Inequalities
  • A.4.1. Schur complements
  • A.4.2. Bounding inequalities
  • A.5. Linear Matrix Inequalities
  • A.5.1. Basics
  • A.5.2. Some standard problems
  • A.5.3. S-procedure
  • A.6. Some Formulas on Matrix Inverses
  • A.6.1. Inverse of block matrices
  • A.6.2. Matrix inversion lemma.