
Closer Look at Fault-Tolerant Control
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Content
- Intro
- Contents
- Preface
- Chapter 1
- Actuator Fault Tolerant Control System for Distillation Processes
- Abstract
- 1. Introduction
- 2. Review of Fault Tolerant Control System
- 2.1. Passive Fault Tolerant Control Systems
- 2.2. Active Fault Tolerant Control Systems
- 2.3. Fault Detection and Diagnosis
- 2.3.1. Model-Based Fault Detection and Diagnosis
- 2.3.1.1. Faulty System Model
- 2.3.1.2. State Estimation Approach
- 2.3.1.2.1. Observer Based Residual Generation
- 2.3.1.2.2. Unknown Input Observer
- 2.3.2. Data-Based Fault Detection and Diagnosis
- 2.3.2.1. Principal Component Analysis
- 2.3.2.2. Dynamic PCA
- 2.3.2.3. Projection to Latent Structure
- 2.4. Fault Tolerant Controllers
- 2.4.1. Fault Tolerant Model Predictive Control (FTMPC)
- 2.4.2. Distributed Model Predictive Control
- 3. Actuator Fault Tolerant Controllers
- 3.1. DPCA FDD Scheme
- 3.2. Control Strategies and Loop Pairing Assessment
- 3.2.1. Relative Gain Array
- 3.2.2. Dynamic Relative Gain Array (DRGA)
- 3.3. Reconfigurable PID Controllers
- 4. Implementation on Distillation Processes
- 4.1. Application to the Shell Heavy Oil Fractionator
- 4.1.1. Process Description and Control Loop Pairing
- 4.1.2. Process Simulation under Fault-Free and Faulty Conditions
- 4.1.3. Actuator Fault Detection and Diagnosis
- 4.1.4. Implementation of FTC on Identified Actuator Fault
- 4.1.5. Results and Discussions
- 4.2. Application to Crude Distillation Unit
- 4.2.1. Crude Distillation Unit Process Description
- 4.2.2. Development and Simulation of Interactive Dynamic Crude Distillation Units
- 4.2.3. Control Strategies Prior Assessment
- 4.2.4. Introduction of Actuator Faults
- 4.2.5. Diagnostic Model Development and Faults Detection and Identification
- 4.2.6. Implementation of the Actuator FTC on CDU for the Identified Actuator Faults
- 4.2.7. Discussion of Results
- Conclusion
- References
- Biographical Sketches
- Chapter 2
- Model-Based Fault-Tolerant Control for Distributed Systems
- Abstract
- 1. Introduction
- 2. Methods
- 2.1. Decentralized Fault-Tolerant Control of Spatially Distributed Systems
- 2.1.1. System Description
- 2.1.2. Decentralized Fault-Tolerant Controller
- 2.1.2.1. DFTC Design: General Case
- 2.1.2.2. DFTC Design: Special Case
- 2.2. Fault Accommodation for Distributed Parameter Systems Represented by Parabolic PDEs
- 2.2.1. System Description
- 2.2.2. Fault Accommodation for DPS with Output Measurements
- 2.2.2.1. Output Feedback Controller Design Under Healthy Conditions
- 2.2.2.2. Actuator Fault Detection and Accommodation
- 2.2.2.3. Sensor Fault Detection and Accommodation
- 2.2.2.4. Time to Accommodation (TTA)
- References
- Chapter 3
- Fault-Tolerant Systems for Unmanned Multirotor Aerial Vehicles
- Chapter 4
- Concepts and Methods in Fault Tolerant Control with Application to a Wind Turbine Simulated System
- Chapter 5
- Reconfıgurable Fault Tolerant Control Agaınst Sensor/Actuator Faults Applıed to Autonomous Underwater Vehıcle Dynamıcs
- Abstract
- 1. Introduction
- 2. Mathematical Model of AUV Steering Dynamics
- 2.1. Steering Subsystem of Sample AUV
- 2.2. Discretization of Steering Subsystem
- 3. KF for Estimation and Identification of AUV Dynamics
- 3.1. Optimum Linear KF for Estimation of AUV Dynamics
- 3.2. Robust Kalman Filter with the Filter Gain Correction
- 3.3. KF for Estimation and Identification of AUV Dynamics
- 4. Sensor/Actuator Fault Detection and Isolation
- 5. Reconfigurable Control Against Actuator Failures
- 6. Simulation Results And Comments
- 6.1. Simulation Results for Sensor/Actuator Fault Detection
- 6.3. OKF Simulation Results
- 6.4. RKF Simulation Results
- 6.5. Reconfigurable Control Simulation Results
- 6.5.1. Conventional LQR Control Results in the Presence of Actuator Faults
- 6.5.2. Reconfigurable LQR Control Results in the Presence of Actuator Faults
- Conclusion
- Acknowledgement
- References
- Biographical Sketches
- Chapter 6
- Self-Organization and Control Reconfiguration of Unmanned Autonomous Systems for Improved Resilience
- Abstract
- 1. Introduction/Motivation
- 2. Technical Approach
- 2.1. Situational Awareness
- 2.2. Fault Diagnosis and Failure Prognosis
- 2.3. Failure Prognosis and Long-Term Prediction
- 2.4. Particle Filtering - A Novel System Estimation Method
- 3. The "Smart" Knowledge Base-A Paradigm in Reasoning
- 4. Resilient Design of Unmanned Autonomous Systems
- 4.1. Definition: Resilient Systems
- 4.2. Resilient Design
- 4.3. The Modeling Framework
- 4.3.1. Graph-Based Approaches
- 4.3.2. Structural and Functional Modeling
- 4.4. Disturbance Factor Analysis
- 4.4.1. Dynamical System Models
- 5. Complex Adaptive Systems: A Rigorous Framework
- for Self-Organization and Control Reconfiguration of Complex Systems for Improved Resilience, Safety and Reliability
- 5.1. Disturbance/Hazard/Threat Analysis
- 5.2. CAS in Unmanned Autonomous Systems
- 5.3. Innovative Features of the Research and Development
- 5.4. Spontaneous Order and Self-Organization
- 6. Self-Organization: An Overview
- 6.1. The Modeling Framework
- 6.2. A Self-Organization Strategy for Unmanned Autonomous Systems
- 6.3. Spectral Graph Theory
- 6.4. Markov Decision Process
- 6.5. Dynamic Programming
- 6.6. Self-Organization Method for a Hexapod
- 6.7. Hexapod Dynamic/Kinematic Model
- 6.8. Failure Mode (Locked Joint Failure)
- 6.9. Hexapod Graph Model
- 6.10. Hexapod Epidemic Spreading Model
- 6.11. Hexapod MDP
- 6.12. Success Criteria (Lyapunov Stability)
- 6.13. Results
- 7. Fault-Tolerant Control (FTC) Strategies
- 7.1. Model Predictive Control (MPC)
- 8. Control Reconfiguration of Unmanned Autonomous Systems
- 8.1. Control Reconfiguration Fundamentals
- 8.2. Control Reconfiguration: The Design Process
- 8.3. The Reconfiguration Strategy
- 8.4. Low-Level Reconfiguration
- 8.5. Mid-Level Redistribution
- 8.6. High-Level Flight/Mission Adaptation
- 8.7. On-Line Reconfiguration: Mission Re-Planning
- 8.8. Receding Horizon Planning
- 8.9. Recursive RHP
- 8.10. Mission Reconfiguration with Goal Changes
- 9. Candidate Platforms
- 9.1. Hovercraft Dynamics Model
- 9.2. Fault Growth Model
- 9.3. Energy Consumption Model
- 9.4. Simulation Results and Discussion
- 10. Simulation Results
- Conclusion
- References
- Chapter 7
- Fault Detection of Nonlinear Networked Control System Based on Multimodal Approach Subject to Induced Delay
- Abstract
- 1. Introduction
- 2. General Information on Multi-Model Approach
- 2.1. Structure of the Multi-Model
- 2.1.1. Coupled Structure
- 2.1.2. Decoupled Structure
- 2.2. Principle of Multi-Model Modeling
- 2.2.1. Building Strategy of Local Models
- 3. Multimodel Modeling of Induction Motor
- 3.1. Database Acquisition
- 3.2. Data Classification
- 3.3. Identification of Local Models
- 3.3.1. Structural Identification
- 3.3.2. Parametric Identification
- 3.4. Fusion
- 3.4.1. Validity Computation
- 3.4.2. Residual Approach
- 4. Case Study: Application of Multi-Model Approach to Modeling Induction Motor
- 4.1. Database Acquisition
- 4.2. Database Clustering
- 4.3. Models Identification
- 4.4. Models Fusion
- 5. Networked Control System
- 6. Diagnostic of Induction Motor in Network
- 7. Simulation Results
- Conclusion
- References
- Chapter 8
- Diagnosis of Sensores Failure in Induction Motor
- Abstract
- Introduction
- 1. Failure Modes of Induction Motor
- The Induction Motors Failure Modes
- 2. Proportionnal-Integral Multiobserver Design
- 3. Induction Motor Sensor Fault Detection and Isolation
- Conclusion
- References
- Index
- Blank Page
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