Efnisyfirlit

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Acknowledgments
• Authors
• Introduction
• Section I Linear and Nonlinear Control
• 1. Linear Systems and Control
• 1.1 Dynamic Systems and Feedback Control
• 1.1.1 Balancing a Stick
• 1.1.2 Simple Day-to-Day Observations
• 1.1.3 Position Control System
• 1.1.4 Temperature Control System
• 1.1.5 Mathematical Modeling of Systems
• 1.1.6 Linear, Time-Invariant, and Lumped Systems
• 1.2 Transfer Functions and State Space Representations
• 1.2.1 Definition: Dynamical Systems
• 1.2.2 Definition: Causal Systems
• 1.2.3 Definition: Linear Systems
• 1.2.4 Time and Frequency Domains
• 1.2.4.1 Definition: Time-Constant
• 1.2.4.2 First-Order Systems
• 1.2.4.3 The Role of Time-Constant
• 1.2.5 Response of Second-Order Systems
• 1.2.5.1 Underdamped Systems
• 1.2.5.2 Critically Damped Systems
• 1.2.5.3 Overdamped Systems
• 1.2.5.4 Higher Order Systems
• 1.2.5.5 A Time Response Analysis Example
• 1.2.5.6 Frequency Response
• 1.2.6 Bode Plots
• 1.2.6.1 Definition: Decibel
• 1.2.6.2 Construction of Bode Plots
• 1.2.7 State Space Representation of Systems
• 1.2.7.1 Two Examples
• 1.2.7.2 Definition: State
• 1.2.7.3 Solution of the State Equation
• 1.3 Stability of Linear Control Systems
• 1.3.1 Bounded Signals
• 1.3.1.1 Definition (a): BIBO Stability
• 1.3.1.2 Definition (b): BIBO Stability
• 1.3.2 Routh-Hurwitz Criterion
• 1.3.2.1 Special Cases
• 1.3.3 Nyquist Criterion
• 1.3.3.1 Polar and Nyquist Plots
• 1.3.3.2 Gain and Phase Margins
• 1.3.3.3 Definition: Gain Crossover Frequency
• 1.3.3.4 Definition: Phase Crossover Frequency
• 1.3.3.5 The Margins on a Bode Plot
• 1.3.4 The Root Locus
• 1.3.4.1 Definition: Root Locus
• 1.3.4.2 The Stability Margin
• 1.4 Design of Control Systems
• 1.4.1 Development of Classical PID Control
• 1.4.1.1 Controller Design Using Root Locus
• 1.4.1.2 Magnitude Compensation
• 1.4.1.3 Angle Compensation
• 1.4.1.4 Validity of Design
• 1.4.1.5 Controller Design Using Bode Plots
• 1.4.1.6 Definition: Bandwidth
• 1.4.1.7 The Design Perspective
• 1.4.1.8 The Lead-Lag Compensator
• 1.4.1.9 PID Implementation
• 1.4.1.10 Reset Windup
• 1.4.2 Modern Pole-Placement
• 1.4.2.1 Controllability
• 1.4.2.2 Definition: Controllability
• 1.4.2.3 Definition: Similarity
• 1.4.2.4 Algorithm: Pole Assignment – SISO Case
• 2. Nonlinear Systems
• 2.1 Nonlinear Phenomena and Nonlinear Models
• 2.1.1 Limit Cycles
• 2.1.2 Bifurcations
• 2.1.3 Chaos
• 2.2 Fundamental Properties of ODEs
• 2.2.1 Autonomous Systems
• 2.2.1.1 Stability of Equilibria
• 2.2.2 Non-Autonomous Systems
• 2.2.2.1 Equilibrium Points
• 2.2.3 Existence and Uniqueness
• 2.3 Contraction Mapping Theorem
• 3. Nonlinear Stability Analysis
• 3.1 Phase Plane Techniques
• 3.1.1 Equilibria of Nonlinear Systems
• 3.2 Poincare-Bendixson Theorem
• 3.2.1 Existence of Limit Cycles
• 3.2.2 QED
• 3.3 Hartman-Grobman Theorem
• 3.4 Lyapunov Stability Theory
• 3.4.1 Lyapunov’s Direct Method
• 3.4.1.1 Positive Definite Lyapunov Functions
• 3.4.1.2 Equilibrium Point Theorems
• 3.4.1.3 Lyapunov Theorem for Local Stability
• 3.4.1.4 Lyapunov Theorem for Global Stability
• 3.4.2 La Salle’s Invariant Set Theorems
• 3.4.3 Krasovskii’s Method
• 3.4.4 The Variable Gradient Method
• 3.4.5 Stability of Non-Autonomous Systems
• 3.4.6 Instability Theorems
• 3.4.7 Passivity Framework
• 3.4.7.1 The Passivity Formalism
• 3.5 Describing Function Analysis
• 3.5.1 Applications of Describing Functions
• 3.5.2 Basic Assumptions
• 4. Nonlinear Control Design
• 4.1 Full-State Linearization
• 4.1.1 Handling Multi-input Systems
• 4.2 Input-Output Linearization
• 4.2.1 Definition: Relative Degree
• 4.2.2 Zero Dynamics and Non-minimum Phase Systems
• 4.2.2.1 Definition: Partially State Feedback Linearizable
• 4.3 Stabilization
• 4.4 Backstepping Control
• 4.5 Sliding Mode Control
• 4.6 Chapter Summary
• Appendix IA
• Appendix IB
• Appendix IC
• Appendix ID
• Exercises for Section I
• References for Section I
• Section II Optimal and H-Infinity Control
• 5. Optimization-Extremization of Cost Function
• 5.1 Optimal Control Theory: An Economic Interpretation
• 5.1.1 Solution for the Optimal Path
• 5.1.2 The Hamiltonian
• 5.2 Calculus of Variation
• 5.2.1 Sufficient Conditions
• 5.2.1.1 Weierstrass Result
• 5.2.2 Necessary Conditions
• 5.3 Euler-Lagrange Equation
• 5.4 Constraint Optimization Problem
• 5.5 Problems with More Variables
• 5.5.1 With Higher Order Derivatives
• 5.5.2 With Several Unknown Functions
• 5.5.3 With More Independent Variables
• 5.6 Variational Aspects
• 5.7 Conversion of BVP to Variational Problem
• 5.7.1 Solution of a Variational Problem Using a Direct Method
• 5.8 General Variational Approach
• 5.8.1 First Order Necessary Conditions
• 5.8.2 Mangasarian Sufficient Conditions
• 5.8.3 Interpretation of the Co-State Variables
• 5.8.4 Principle of Optimality
• 5.8.5 General Terminal Constraints
• 5.8.5.1 Necessary Conditions for Equality Terminal Constraints
• Appendix 5A
• 6. Optimal Control
• 6.1 Optimal Control Problem
• 6.1.1 Dynamic System and Performance Criterion
• 6.1.2 Physical Constraints
• 6.1.2.1 Point Constraints
• 6.1.2.2 Isoperimetric Constraints
• 6.1.2.3 Path Constraints
• 6.1.3 Optimality Criteria
• 6.1.4 Open Loop and Closed Loop Optimal Control
• 6.2 Maximum Principle
• 6.2.1 Hamiltonian Dynamics
• 6.2.2 Pontryagin Maximum Principle
• 6.2.2.1 Fixed Time, Free Endpoint Problem
• 6.2.2.2 Free Time, Fixed Endpoint Problem
• 6.2.3 Maximum Principle with Transversality Conditions
• 6.2.4 Maximum Principle with State Constraints
• 6.3 Dynamic Programming
• 6.3.1 Dynamic Programming Method
• 6.3.2 Verification of Optimality
• 6.3.3 Dynamic Programming and Pontryagin Maximum Principle
• 6.3.3.1 Characteristic Equations
• 6.3.3.2 Relation between Dynamic Programming and the Maximum Principle
• 6.4 Differential Games
• 6.4.1 Isaacs’s Equations and Maximum Principle/Dynamic Programming in Games
• 6.5 Dynamic Programming in Stochastic Setting
• 6.6 Linear Quadratic Optimal Regulator for Time-Varying Systems
• 6.6.1 Riccati Equation
• 6.6.2 LQ Optimal Regulator for Mixed State and Control Terms
• 6.7 Controller Synthesis
• 6.7.1 Dynamic Models
• 6.7.2 Quadratic Optimal Control
• 6.7.2.1 Linear Quadratic Optimal State Regulator
• 6.7.2.2 Linear Quadratic Optimal Output Regulator
• 6.7.3 Stability of the Linear Quadratic Controller/Regulator
• 6.7.4 Linear Quadratic Gaussian (LQG) Control
• 6.7.4.1 State Estimation and LQ Controller
• 6.7.4.2 Separation Principle and Nominal Closed Loop Stability
• 6.7.5 Tracking and Regulation with Quadratic Optimal Controller
• 6.7.5.1 Transformation of the Model for Output Regulation and Tracking
• 6.7.5.2 Unmeasured Disturbances and Model Mismatch
• 6.7.5.3 Innovations Bias Approach
• 6.7.5.4 State Augmentation Approach
• 6.8 Pole Placement Design Method
• 6.9 Eigenstructure Assignment
• 6.9.1 Problem Statement
• 6.9.2 Closed Loop Eigenstructure Assignment
• 6.10 Minimum-Time and Minimum-Fuel Trajectory Optimization
• 6.10.1 Problem Definition
• 6.10.2 Parameterization of the Control Problem
• 6.10.3 Control Profile for Small α
• 6.10.4 Determination of Critical α
• Appendix 6A
• 7. Model Predictive Control
• 7.1 Model-Based Prediction of Future Behavior
• 7.2 Innovations Bias Approach
• 7.3 State Augmentation Approach
• 7.4 Conventional Formulation of MPC
• 7.5 Tuning Parameters
• 7.6 Unconstrained MPC
• 7.7 Quadratic Programming (QP) Formulation of MPC
• 7.8 State-Space Formulation of the MPC
• 7.9 Stability
• Appendix 7A
• Appendix 7B
• 8. Robust Control
• 8.1 Robust Control of Uncertain Plants
• 8.1.1 Robust Stability and HI Norm
• 8.1.2 Disturbance Rejection and Loop-Shaping Using HI Control
• 8.2 H2 Optimal Control
• 8.2.1 The Optimal State Feedback Problem
• 8.2.2 The Optimal State Estimation Problem
• 8.2.3 The Optimal Output Feedback Problem
• 8.2.4 H2 Optimal Control against General Deterministic Inputs
• 8.2.5 Weighting Matrices in H2 Optimal Control
• 8.3 H∞ Control
• 8.3.1 H∞ Optimal State Feedback Control
• 8.3.2 H∞ Optimal State Estimation
• 8.3.3 H∞ Optimal Output Feedback Problem
• 8.3.4 The Relation between S, P and Z
• 8.4 Robust Stability and H∞ Norm
• 8.5 Structured Uncertainties and Structured Singular Values
• 8.6 Robust Performance Problem
• 8.6.1 The Robust HI Performance Problem
• 8.6.2 The Robust H2 Performance Problem
• 8.7 Design Aspects
• 8.7.1 Some Considerations
• 8.7.2 Basic Performance Limitations
• 8.7.3 Application of H∞ Optimal Control to Loop Shaping
• Appendix 8A
• Appendix IIA
• Appendix IIB
• Appendix IIC
• Exercises for Section II
• References for Section II
• Section III Digital and Adaptive Control
• 9. Discrete Time Control Systems
• 9.1 Representation of Discrete Time System
• 9.1.1 Numerical Differentiation
• 9.1.2 Numerical Integration
• 9.1.3 Difference Equations
• 9.2 Modeling of the Sampling Process
• 9.2.1 Finite Pulse Width Sampler
• 9.2.2 An Approximation of the Finite Pulse Width Sampling
• 9.2.3 Ideal Sampler
• 9.3 Reconstruction of the Data
• 9.3.1 Zero Order Hold
• 9.3.2 First Order Hold
• 9.4 Pulse Transfer Function
• 9.4.1 Pulse Transfer Function of the ZOH
• 9.4.2 Pulse Transfer Function of a Closed Loop System
• 9.4.3 Characteristics Equation
• 9.5 Stability Analysis in z-Plane
• 9.5.1 Jury Stability Test
• 9.5.2 Singular Cases
• 9.5.3 Bilinear Transformation and Routh Stability Criterion
• 9.5.4 Singular Cases
• 9.6 Time Responses of Discrete Time Systems
• 9.6.1 Transient Response Specifications and Steady-State Error
• 9.6.2 Type-n Discrete Time Systems
• 9.6.3 Study of a Second Order Control System
• 9.6.4 Correlation between Time Response and Root Locations in s- and z-Planes
• 9.6.5 Dominant Closed Loop Pole Pairs
• Appendix 9A
• 10. Design of Discrete Time Control Systems
• 10.1 Design Based on Root Locus Method
• 10.1.1 Rules for Construction of the Root Locus
• 10.1.2 Root Locus of a Digital Control System
• 10.1.3 Effect of Sampling Period T
• 10.1.4 Design Procedure
• 10.2 Frequency Domain Analysis
• 10.2.1 Nyquist Plot
• 10.2.2 Bode Plot, and Gain and Phase Margins
• 10.3 Compensator Design
• 10.3.1 Phase Lead, Phase Lag, and Lag-Lead Compensators
• 10.3.2 Compensator Design Using Bode Plot
• 10.3.2.1 Phase Lead Compensator
• 10.3.2.2 Phase Lag Compensator
• 10.3.2.3 Lag-Lead Compensator
• 10.4 Design with Deadbeat Response
• 10.4.1 DBR Design of a System When the Poles and Zeros Are in the Unit Circle
• 10.4.1.1 Physical Realizability of the Controller Dc(z)
• 10.4.2 DBR When Some of the Poles and Zeros Are on or outside the Unit Circle
• 10.4.3 Sampled Data Control Systems with DBR
• 10.5 State Feedback Controller
• 10.5.1 Designing K by Transforming the State Model into Controllable Canonical Form
• 10.5.2 Designing K by Ackermann’s Formula
• 10.5.3 Set Point Tracking
• 10.5.4 State Feedback with Integral Control
• 10.6 State Observers
• 10.6.1 Full Order Observers
• 10.6.1.1 Open Loop Estimator
• 10.6.1.2 Luenberger State Observer
• 10.6.1.3 Controller with Observer
• 10.6.2 Reduced Order Observers
• 10.6.3 Controller with Reduced Order Observer
• 10.6.4 Deadbeat Control by State Feedback and Deadbeat Observer
• 10.6.5 Incomplete State Feedback
• 10.6.6 Output Feedback Design
• 10.7 Optimal Control
• 10.7.1 Discrete Euler-Lagrange Equation
• 10.7.2 Linear Quadratic Regulator
• 11. Adaptive Control
• 11.1 Direct and Indirect Adaptive Control Methods
• 11.1.1 Adaptive Control and Adaptive Regulation
• 11.2 Gain Scheduling
• 11.2.1 Classical GS
• 11.2.2 LPV and LFT Synthesis
• 11.2.3 Fuzzy Logic-Based Gain Scheduling (FGS)
• 11.3 Parameter Dependent Plant Models
• 11.3.1 Linearization Based GS
• 11.3.2 Off Equilibrium Linearizations
• 11.3.3 Quasi LPV Method
• 11.3.4 Linear Fractional Transformation
• 11.4 Classical Gain Scheduling
• 11.4.1 LTI Design
• 11.4.2 GS Controller Design
• 11.4.2.1 Linearization Scheduling
• 11.4.2.2 Interpolation Methods
• 11.4.2.3 Velocity Based Scheduling
• 11.4.3 Hidden Coupling Terms
• 11.4.4 Stability Properties
• 11.5 LPV Controller Synthesis
• 11.5.1 LPV Controller Synthesis Set Up
• 11.5.1.1 Stability and Performance Analysis
• 11.5.2 Lyapunov Based LPV Control Synthesis
• 11.5.3 LFT Synthesis
• 11.5.4 Mixed LPV-LFT Approaches
• 11.6 Fuzzy Logic-Based Gain Scheduling
• 11.7 Self-Tuning Control
• 11.7.1 Minimum Variance Regulator/Controller
• 11.7.2 Pole Placement Control
• 11.7.3 A Bilinear Approach
• 11.8 Adaptive Pole Placement
• 11.9 Model Reference Adaptive Control/Systems (MRACS)
• 11.9.1 MRAC Design of First Order System
• 11.9.2 Adaptive Dynamic Inversion (ADI) Control
• 11.9.3 Parameter Convergence and Comparison
• 11.9.4 MRAC for n-th Order System
• 11.9.5 Robustness of Adaptive Control
• 11.10 A Comprehensive Example
• 11.10.1 The Underlying Design Problem for Known Systems
• 11.10.2 Parameter Estimation
• 11.10.3 An Explicit Self-Tuner
• 11.10.4 An Implicit Self-Tuner
• 11.10.5 Other Implicit Self-Tuners
• 11.11 Stability, Convergence, and Robustness Aspects
• 11.11.1 Stability
• 11.11.2 Convergence
• 11.11.2.1 Martingale Theory
• 11.11.2.2 Averaging Methods
• 11.12 Use of the Stochastic Control Theory
• 11.13 Uses of Adaptive Control Approaches
• 11.13.1 Auto-Tuning
• 11.13.2 Automatic Construction of Gain Schedulers and Adaptive Regulators
• 11.13.3 Practical Aspects and Applications
• 11.13.3.1 Parameter Tracking
• 11.13.3.2 Estimator Windup and Bursts
• 11.13.3.3 Robustness
• 11.13.3.4 Numerics and Coding
• 11.13.3.5 Integral Action
• 11.13.3.6 Supervisory Loops
• 11.13.3.7 Applications
• Appendix 11A
• Appendix 11B
• Appendix 11C
• 12. Computer-Controlled Systems
• 12.1 Computers in Measurement and Control
• 12.2 Components in Computer-Based Measurement and Control System (CMCS)
• 12.3 Architectures
• 12.3.1 Centralized Computer Control System
• 12.3.2 Distributed Computer Control Systems (DDCS)
• 12.3.3 Hierarchical Computer Control Systems
• 12.3.4 Tasks of Computer Control Systems and Interfaces
• 12.3.4.1 HMI-Human Machine Interface
• 12.3.4.2 Hardware for Computer-Based Process/Plant Control System
• 12.3.4.3 Interfacing Computer System with Plant
• 12.4 Smart Sensor Systems
• 12.4.1 Components of Smart Sensor Systems
• 12.5 Control System Software and Hardware
• 12.5.1 Embedded Control Systems
• 12.5.2 Building Blocks
• 12.5.2.1 Software and Hardware Building Blocks
• 12.5.2.2 Appliance/System Building Blocks
• 12.6 ECS-Implementation
• 12.7 Aspects of Implementation of a Digital Controller
• 12.7.1 Representations and Realizations of the Digital Controller
• 12.7.1.1 Pre-Filtering and Computational Delays
• 12.7.1.2 Nonlinear Actuators
• 12.7.1.3 Antiwindup with an Explicit Observer
• 12.7.2 Operational and Numerical Aspects
• 12.7.3 Realization of Digital Controllers
• 12.7.3.1 Direct/Companion Forms
• 12.7.3.2 Well-Conditioned Form
• 12.7.3.3 Ladder Form
• 12.7.3.4 Short-Sampling-Interval Modification and δ-Operator Form
• 12.7.4 Programming
• Appendix III
• Exercises for Section III
• References for Section III
• Section IV AI-Based Control
• 13. Introduction to AI-Based Control
• 13.1 Motivation for Computational Intelligence in Control
• 13.2 Artificial Neural Networks
• 13.2.1 An Intuitive Introduction
• 13.2.2 Perceptrons
• 13.2.3 Sigmoidal Neurons
• 13.2.4 The Architecture of Neural Networks
• 13.2.5 Learning with Gradient Descent
• 13.2.5.1 Issues in Implementation
• 13.2.6 Unsupervised and Reinforcement Learning
• 13.2.7 Radial Basis Networks
• 13.2.7.1 Information Processing of an RBF Network
• 13.2.8 Recurrent Neural Networks
• 13.2.9 Towards Deep Learning
• 13.2.10 Summary
• 13.3 Fuzzy Logic
• 13.3.1 The Linguistic Variables
• 13.3.2 The Fuzzy Operators
• 13.3.3 Reasoning with Fuzzy Sets
• 13.3.4 The Defuzzification
• 13.3.4.1 Some Remarks
• 13.3.5 Type II Fuzzy Systems and Control
• 13.3.5.1 MATLAB Implementation
• 13.3.6 Summary
• 13.4 Genetic Algorithms and Other Nature Inspired Methods
• 13.4.1 Genetic Algorithms
• 13.4.2 Particle Swarm Optimization
• 13.4.2.1 Accelerated PSO
• 13.4.3 Summary
• 13.5 Chapter Summary
• 14. ANN-Based Control Systems
• 14.1 Applications of Radial Basis Function Neural Networks
• 14.1.1 Fully Tuned Extended Minimal Resource Allocation Network RBF
• 14.1.2 Autolanding Problem Formulation
• 14.2 Optimal Control Using Artificial Neural Network
• 14.2.1 Neural Network LQR Control Using the Hamilton-Jacobi-Bellman Equation
• 14.2.2 Neural Network H∞ Control Using the Hamilton-Jacobi-Isaacs Equation
• 14.3 Historical Development
• Appendix 14A
• Appendix 14B
• 15. Fuzzy Control Systems
• 15.1 Simple Examples
• 15.2 Industrial Process Control Case Study
• 15.2.1 Results
• 15.3 Chapter Summary
• Appendix 15A
• Appendix 15B
• 16. Nature Inspired Optimization for Controller Design
• 16.1 Control Application in Light Energy Efficiency
• 16.1.1 A Control Systems Perspective
• 16.2 PSO Aided Fuzzy Control System
• 16.3 Genetic Algorithms (GAs) Aided Semi-Active Suspension System
• 16.4 GA Aided Active Suspension System
• 16.5 Training ANNs Using GAs
• 16.6 Chapter Summary
• Appendix 16A
• Appendix 16B
• Appendix IVA
• Exercises for Section IV
• References for Section IV
• Section V System Theory and Control Related Topics
• Appendix A
• Appendix B
• Appendix C
• Appendix D
• Appendix E
• Appendix F
• Appendix G
• Index