ENCYCLOPEDIA OF CONTROL SYSTEMS, ROBOTICS, AND AUTOMATION - Table of Contents


BASIC ELEMENTS OF CONTROL SYSTEMS

Dynamical Systems

Graphical Description of Systems

Open-loop Control and Closed-loop Control

Principal Functions of Control

The Basic Structure of Control Systems

Some Typical Examples of Control

A Brief Overview of the History of Control Systems

GENERAL MODELS OF DYNAMICAL SYSTEMS

Mathematical Models

Dynamic and Static Behavior of Systems

System Properties

DESCRIPTION OF CONTINUOUS LINEAR TIME-INVARIANT SYSTEMS IN TIME-DOMAIN

Description by differential equations

System description with reference to special signals

System description in state space

DESCRIPTION OF CONTINUOUS LINEAR TIME-INVARIANT SYSTEMS IN FREQUENCY DOMAIN

Laplace Transformation

Fourier Transformation

Transfer Function of a Dynamical System

Frequency-Response of a Dynamical System

The Most Common Dynamical Systems

CLOSED-LOOP BEHAVIOUR OF CONTINUOUS LINEAR TIME-INVARIANT SYSTEMS

Dynamic behavior of the closed-loop system

Sensitivity of feedback control systems to parameter variations

Stability

Steady-state error

PID controller and other standard controller types

Behavior of Standard Controllers in Closed-Loop Operation.

CLASSICAL DESIGN METHODS FOR CONTINUOUS LTI-SYSTEMS

Introduction

CONTROLLER DESIGN IN TIME-DOMAIN

Problem formulation

Time-domain performance specifications

Optimal controller settings subject to the ISE-criterion

Empirical procedures

Mixed time- and frequency-domain design by standard polynomials

Concluding Remarks

DESIGN IN THE FREQUENCY DOMAIN

Gain and Phase Margins

Types of Compensators

Design of PI and Lag Compensators

Design of PD Compensators (Realized by Rate Feedback)

Design of Lead Compensators

Design of PID Compensators

Design of Lag - Lead Compensators

PID CONTROL

Process Models

Performance Evaluation of PID Control Systems

Action Modes of PID Controllers

Design of PID Control Systems

Advanced Topics

INTERNAL MODEL CONTROL

The Internal Model Control Structure

Internal Model Control Design Procedure

Application of IMC design to Simple Models

IMC-PID tuning Rules for First-Order with Delay Plants

Additional IMC Design Topics

SMITH PREDICTOR AND ITS MODIFICATIONS

Controller design

Performance comparison

Modification for high order systems

Modification for rapid load rejection

Modifications for open-loop unstable systems

DISCRETE-TIME, SAMPLED-DATA, DIGITAL CONTROL SYSTEMS, AND QUANTIZATION EFFECTS

Discrete-Time Systems

Sampled-Data Systems

Digital Control Systems

Quantization Effects

DISCRETE-TIME EQUIVALENTS TO CONTINUOUS-TIME SYSTEMS

Design of Discrete-Time Control Systems for Continuous-Time Plants

Discrete-Time Equivalents of Continuous-Time Plants

Discretizing Continuous-Time Controllers

Discretization of Continuous-Time State Variable Models

DESIGN METHODS FOR DIGITAL CONTROLLERS, SAMPLE-RATE

Design Methods for Digital Controllers

Sample Rate

REAL-TIME IMPLEMENTATION

A Simple Real-Time System

Computational Delay and Jitter

Real-Time Integration of Continuous-Time States

Implementation on Fixed-Point Processors

Implementation on Floating-Point Processors

Real-Time Operating Systems

Intertask Communication in Multitasking Systems

Distributed Real-Time Systems

Time Triggered Systems for Safety Critical Applications

Development Tools for Real-Time Implementation

DESCRIPTION AND ANALYSIS OF DYNAMIC SYSTEMS IN STATE SPACE

Extraction of the State Space Representation from the Transfer Function G(s)

Transformation to Diagonal Form

Solution of the State Equations

Stability

Controllability and Observability

Discrete Time Systems

CONTROLLER DESIGN

Objectives and Structure of State Feedback Control

Determination of the pre-compensator g

Determination of the Controller k

Example: Inverted Pendulum

Discrete-Time State Feedback and Dead-Beat Behavior

OBSERVER DESIGN

Objectives and Structure of the State Observer

Design of the Observer

Example: Inverted Pendulum

The Observer in Closed-Loop Control- The Separation Principle

Reduced Order Observer

Discrete- Time Observers

EXTENDED CONTROL STRUCTURES

Steady State Behaviour under realistic assumptions

PI- State Feedback Control

Model-based dynamic pre-compensator

DESCRIBING FUNCTION METHOD

The Sinusoidal Describing Function

The Evaluation of some DFs

Limit Cycles and Their Stability

DF Accuracy

Some Examples of DF Usage

Closed Loop Frequency Response

Compensator Design

Additional Aspects

Conclusions

SECOND ORDER SYSTEMS

Basic Principles

Analysis Using the Phase Plane

Conclusions

STABILITY THEORY

Linearization: Stability in the First Approximation

The Direct Method of Lyapunov

POPOV AND CIRCLE CRITERION

Kalman-Yakubovich-Lemma

Criteria for Absolute Stability

CONTROL BY COMPENSATION OF NONLINEARITIES

Plants with Actuator Nonlinearities

Parameterized Inverses

State Feedback Designs

Output Feedback Inverse Control

Output Feedback Designs

Designs for Unknown Linear Dynamics

Designs for Multivariable Systems

Designs for Nonlinear Dynamics

Neural Network based Adaptive Inverse Compensation

An illustrative Example

Concluding Remarks

ESTIMATION AND COMPENSATION OF NONLINEAR PERTURBATION BY DISTURBANCE OBSERVERS

Problem Statement

Theory

Applications

ANTI WINDUP AND OVERRIDE CONTROL

PI-Control with Input Saturations

Plants of dominant Second Order

Output Constrained Control

Introduction

Conclusion and Outlook

GAIN-SCHEDULING

Linearization Theory

Divide and Conquer Gain-Scheduling Design

LPV Gain-Scheduling

Outlook

SOME BASICS IN MODELING OF MECHATRONIC SYSTEMS

System Variables and System Elements

Kirchhoff Networks

Port-Hamiltonian Systems

MODELING AND SIMULATION OF DISTRIBUTED PARAMETER SYSTEMS

Modeling of distributed parameter systems

Simulation of distributed parameter systems

MODELING AND SIMULATION OF LARGE-SCALE HYBRID SYSTEMS

General Concepts

System Representations and Software Tools

Object-oriented Modeling of Physical Systems

Integration of Complex Discrete Event and Object-Oriented Models

Ongoing Research and Future Challenges

MODELING AND SIMULATION OF DYNAMIC SYSTEMS USING BOND GRAPHS

Early history

Modeling and simulation of dynamic behavior of physical systems

Key aspects of the port-based approach

Bond Graph Notation

Port-based modeling and simulation of dynamic behavior of physical systems in terms of bond graphs: a simple example

Future trends

RAPID PROTOTYPING FOR MODEL, AND CONTROLLER IMPLEMENTATION

Definition of Rapid Prototyping

Goals

General solution

Simulation acceleration

Conclusions

MODELING LANGUAGES FOR CONTINUOUS AND DISCRETE SYSTEMS

Aims of Modeling Languages

Historical background

A Modeling Approach

Modeling Languages

A comparison of VHDL-AMS and Modelica

Conclusions

SIMULATION SOFTWARE - DEVELOPMENT AND TRENDS

Continuous Roots of Simulation

CSSL Structure in Continuous Simulation

Numerical Algorithms in Simulation Systems

Simulation Software and CACSD Tools

Analysis Methods in Simulation Systems

Implicit Models -Algebraic Loops -Differential-Algebraic Equations

Discrete Elements in Continuous Modeling and Simulation

Hybrid modeling and simulation - Combined Modeling and Simulation

Simulation in Specific Domains

Developments beyond CSSL

Discrete Event Simulation

Object-oriented Approaches to Modeling and Simulation

Choice and Comparison of Simulation Software

Conclusion

MEASUREMENTS OF FREQUENCY RESPONSE FUNCTIONS

An introduction to the discrete Fourier transform

Spectral representation of periodic signals

Analysis of FRF measurements using periodic excitations

Reducing FRF measurement errors for periodic excitations

FRF measurements using random excitations

FRF measurements of multiple input multiple output systems

Guidelines for FRF measurements

Conclusions

ESTIMATION WITH KNOWN NOISE MODEL

Estimation Algorithms - General

Estimation Algorithms - Specific

Illustration and Overview of the Properties

Extensions

Model Selection - Model Validation

Introduction

FREQUENCY DOMAIN SUBSPACE ALGORITHMS

Model equations

Subspace algorithms

Practical remarks

Simulation examples

Real measurement example

ESTIMATION WITH UNKNOWN NOISE MODEL

Estimation algorithms

Overview and Illustration of the properties

Identification of parametric noise models

Identification in feedback

Model selection

Introduction

MODAL ANALYSIS

The “Modal” Model

Frequency-Domain Identification of Modes

Application

Conclusion

LEAST SQUARES AND INSTRUMENTAL VARIABLE METHODS

Models as predictors

Estimating the model parameters

Stochastic analysis

Instrumental variable method

Computing the estimate

Multivariable systems

Optimal weighted LS estimator

PREDICTION ERROR METHODS

Description

Properties

SUBSPACE IDENTIFICATION METHODS

Notation

Geometric Tools

Deterministic subspace identification

Stochastic subspace identification

Combined deterministic-stochastic subspace identification algorithm

Comments and perspectives

Software

Introduction

RECURSIVE ALGORITHMS

Recursive Algorithm for Constant Coefficients

Convergence of Estimates

Time-Varying Systems

Concluding Remarks

IDENTIFICATION FOR CONTROL

Identification of approximate models

Identification from closed-loop data

Iterative Identification and Control

Extensions

Introduction

Conclusions

CONTINUOUS-TIME IDENTIFICATION

A model transformation

Noise Modeling

Parameter Estimation

Statistical Consistency and Convergence

IDENTIFIABILITY OF LINEAR CLOSED-LOOP SYSTEMS

Identifiability Concepts

Identifiability Conditions for Closed-Loop Systems -A Short Overview

Complete and Partial I/O-Identifiability of Multivariable Closed-Loop Systems

Conclusions

RELATIONS BETWEEN TIME DOMAIN AND FREQUENCY DOMAIN PREDICTION ERROR METHODS

Prediction error methods

Discussion

Numerical example

Conclusions

Introduction

IDENTIFICATION OF TIME VARYING SYSTEMS

Simple Limited Memory Algorithms

Modeling the Parameter Variations: The Dynamic Transfer Function (DTF) Model

Illustrative Examples

Conclusions

NONPARAMETRIC SYSTEM IDENTIFICATION

Representation of Nonlinear Systems

Identification of Wiener Kernels

Identification of Volterra Kernels

Frequency Domain Approach

IDENTIFICATION OF BLOCK-ORIENTED MODELS

The building blocks

Hammerstein models

Wiener models

Other feedforward structures

Qualitative behavior of feedforward structures.

Feedback block-oriented structures

Practical issues in model building

Concluding Remarks

IDENTIFICATION OF NARMAX AND RELATED MODELS

System Identification

Nonlinear Models vs. Linear Models

The NARMAX model

Practical Implementations of the NARMAX model

The NARMAX Method

Mapping the NARMAX Model in the Frequency Domain

A Practical Example

Conclusions

SYSTEM IDENTIFICATION USING NEURAL NETWORKS

Artificial Neural Networks

System Identification using Artificial Neural Networks

SYSTEM IDENTIFICATION USING FUZZY MODELS

Nonlinear Dynamic Models for System Identification

Fuzzy Models

Identification of Fuzzy Models

Illustrative Example

Conclusions

SYSTEM IDENTIFICATION USING WAVELETS

Wavelets - A Brief Overview

System Identification

System Identification using Wavelets

Conclusions

PARAMETER ESTIMATION FOR DIFFERENTIAL EQUATIONS

The Hartley Transformation

The Hartley Modulating Functions

Formulation of the parameter estimation equation

Computational Issues

Illustrative Examples

Application to an Inverted Pendulum Model

Conclusions

PARAMETER ESTIMATION FOR NONLINEAR CONTINUOUS-TIME STATE-SPACE MODELS FROM SAMPLED DATA

Mathematical Preliminaries

The Prediction-Error Approach to Parameter Estimation

State-Space Models and State Estimation

Parameter Estimation for State-Space Models

Conclusion

IDENTIFICATION IN THE FREQUENCY DOMAIN

Linear System Identification

Nonlinear System Identification

Conclusions for Nonlinear System Identification

PARAMETRIC IDENTIFICATION USING SLIDING MODES

State Identification

Parameter Identification

State and parameter identification

Simulations results

Conclusion

LINEAR-MODEL CASE

Bounding a linear model: the simplest case

Computation of the exact feasible set

Approximate parameter bounding

Parameter bounding with unknown output-error bound

Parameter bounding with uncertain explanatory-variables vector

Clashes and outliers

Parameter bounds for time-varying linear systems

Conclusions

NONLINEAR-MODEL CASE

Definitions and notation

Classification of non-linear parameter bounding algorithms

Example

Concluding Remarks

CONTROL OF LINEAR MULTIVARIABLE SYSTEMS

Linear Multivariable Systems

Control System Example

DESCRIPTION AND CLASSIFICATION IN MIMO DESIGN

Models

Control Systems Design

Translating SISO concepts into MIMO world

Frequency Domain Design techniques

Time Domain Design Approaches

Non-standard MIMO Problems

CANONICAL FORMS FOR STATE SPACE DESCRIPTIONS

State - Space Representations, Matrix Pencils, and State - Space Transformations

Matrix Pencils and Kronecker Form

Canonical Form under Similarity: Autonomous Descriptions with no outputs

Kronecker Form under the Full State Space Transformation Group

Brunovsky Canonical Forms under Coordinate and Feedback Transformations

Canonical Forms under Coordinate Transformations

Conclusions

MULTIVARIABLE POLES AND ZEROS

System Representations and Classification

Background on Polynomial matrices and Matrix Pencils

Finite Poles and Zeros of State Space Models: Dynamics and their Geometry

Finite Poles and Zeros of Transfer Function Models

Infinite Poles and Zeros

Algebraic Function Characterization of Poles and Zeros

Zero Structure Formation in Systems Design

FREQUENCY DOMAIN REPRESENTATION AND SINGULAR VALUE DECOMPOSITION

Preliminaries

External and internal representations of linear systems

Time and frequency domain interpretation of various norms

POLYNOMIAL AND MATRIX FRACTION DESCRIPTION

Scalar Systems

Multivariable Systems

Conclusion

SYSTEM CHARACTERISTICS: STABILITY, CONTROLLABILITY, OBSERVABILITY

Mathematical model

Stability

Controllability

Observability

Conclusions

MODEL REDUCTION

What is Model Reduction?

Linear System Properties

Model Reduction by Truncation

Model Reduction by Optimization

A Glimpse on the Multi-Component Model Reduction Problem

Tutorial Examples

FULL-ORDER STATE OBSERVERS

Linear Observers

The Separation Principle

Nonlinear Observers

REDUCED-ORDER STATE OBSERVERS

Linear, Reduced-Order Observers

Nonlinear Reduced-Order Observers

KALMAN FILTERS

White Noise

Linear Estimation

The Linear Optimal Estimator in Discrete Time (Kalman Filter)

The Continuous-Time Optimal Estimator (Kalman-Bucy Filter)

Nonlinear Estimation

Implementation Methods

Present and Future Applications of the Kalman Filter

POLE PLACEMENT CONTROL

Separation of state observation and state feedback

The single-input case

The multi-input case

EIGENSTRUCTURE ASSIGNMENT FOR CONTROL

Definition of Eigenstructure Assignment

Role of the System Eigenstructure

Freedom for Eigenstructure Assignment

Allowable Eigenvector Subspaces

Calculation of Controller Matrices

Assignment of Desired Eigenvectors

Compromise between Eigenvalues and Eigenvectors

Parametric Eigenstructure Assignment

Multiobjective Robust Eigenstructure Assignment

Various Eigenstructure Assignment Techniques

OPTIMAL LINEAR QUADRATIC CONTROL

The LQ regulator in continuous time

The steady-state LQ regulator in continuous time

Properties of the steady-state LQ regulator in continuous time

The LQ regulator in discrete time

Numerical methods

PONTRYAGIN'S MAXIMUM PRINCIPLE

An Example

The problem of Optimal Control

A More Rigorous Formulation of the Problem

The Maximum Principle

A Discussion

The Time-Optimal Control Problem

Time-Optimal Control for Linear Systems

Other Performance Indices

Interpretations and generalizations of the Maximum Principle

DECOUPLING CONTROL

Control of a Heat Exchanger

Dynamic Decoupling

Static decoupling

Process Control Decoupling Design

Other Topics

Introduction

CONTROLLER DESIGN USING POLYNOMIAL MATRIX DESCRIPTION

Polynomial Approach To Three Classical Control Problems

Numerical Methods for Polynomial Matrices

Conclusion

DESIGN TECHNIQUES IN THE FREQUENCY DOMAIN

Frequency Responses and Stability

Basic Design

A Design Example for an Unstable Chemical Reactor

DESIGN TECHNIQUES FOR TIME-VARYING SYSTEMS

Model Descriptions

Stabilization Techniques

Causal information controllers

SERVO CONTROL DESIGN

Classical Servo Control Design

Modern Servo Control Design

Conclusions

UNCERTAINTY MODELS FOR ROBUSTNESS ANALYSIS

Notation and definitions

Uncertainty representation and robustness problems

Unstructured uncertainty models

Structured uncertainty models

Highly structured (parametric) uncertainty models

State space uncertainty models

Conclusions

ROBUSTNESS UNDER REAL PARAMETER UNCERTAINTY

Notations and Preliminaries

Real Parameter Stability Margin

Extremal Results in Parametric Robust Control Theory

Frequency Domain Analysis of Uncertain Systems

Robust Classical Controller Design

H-OPTIMAL CONTROL

The Minimum Sensitivity Problem

Robustness and the Sensitivity Functions

The Mixed Sensitivity Problem

The Standard Problem and its Solutions

Application to Robust Control System Design

L1 ROBUST CONTROL

The l1 Norm

Robustness To Signal Uncertainty: The l1 Norm Minimization Problem

Robustness to Unmodeled Dynamics

MU-SYNTHESIS

Control Design via D - K Iteration

Control Design Using Fixed-Order Scalings

Conclusion

CONTROLLER DESIGN USING LINEAR MATRIX INEQUALITIES

Design Specifications and Linear Matrix Inequalities

Controller Design Using Linear Matrix Inequalities

Illustrative Design Example: Robust Control of a Power System Stabilizer

Conclusion

ROBUST CONTROL OF NONLINEAR SYSTEMS: A CONTROL LYAPUNOV FUNCTION APPROACH

Robust Control Lyapunov Function (RCLF)

Disturbance attenuation

Construction of RCLFs by Backstepping

Cost-to-Come Function for Output Feedback

FUNDAMENTALS OF THE QUANTITATIVE FEEDBACK THEORY TECHNIQUE

The MISO Analog Control Systems

The MISO Discrete Control System

MIMO Systems

MIMO QFT With External (Input) Disturbance(s)

QFT Application

Introduction

RELAY AUTOTUNING OF PID CONTROLLERS

Relay Autotuning

Analysis of Relay Autotuning using the DF method

Controller Design Based on the Critical Point

Further Considerations

Conclusions

SELF-TUNING CONTROL

Categorization of Self-Tuning Controllers.

Implicit generalized minimum variance control

Practical issues

Examples

Future prospects

MODEL REFERENCE ADAPTIVE CONTROL

Dynamic Models

Model Reference Adaptive Control

Parameter Identification

ADAPTIVE PREDICTIVE CONTROL

System models and long-range prediction

The GPC control law

Robustness analysis

Self-tuning aspects

Conclusions

STOCHASTIC ADAPTIVE CONTROL

Adaptive Control of Markov Chains

Adaptive Control of ARMAX models

Adaptive Control of Continuous Time Linear Stochastic Systems

Some Generalizations of Adaptive Control

Conclusions

ADAPTIVE DUAL CONTROL

Stochastic Adaptive Control

Optimal Dual Controllers

Suboptimal Dual Controllers

When To Use Dual Control?

ADAPTIVE NONLINEAR CONTROL

Backstepping

Tuning Functions Design: Examples

General Recursive Design: Procedure

Modular Design

Conclusions

CONTROL OF INTERMITTENT PROCESSES

Definitions, physical and mathematical models

Repetitive and iterative learning control schemes

Designing ILC for real world applications

Robustness issues and focus of research

Industrial application examples

Conclusion

MODEL BASED PREDICTIVE CONTROL FOR LINEAR SYSTEMS

The MBPC Principle

SISO MBPC

Extensions

MIMO MBPC

Constrained Control

NONLINEAR MODEL PREDICTIVE CONTROL

Theoretical Aspects of NMPC

Computational Aspects of NMPC

Introduction

Conclusions and Outlook

MODELS OF STOCHASTIC SYSTEMS

Random variables

Description of stochastic process

Finite dimensional approximations

Mixed stochastic-deterministic systems

Stochastic differential equations

STOCHASTIC STABILITY

Stability and Liapunov Functions

The Stochastic Problem: Definitions and Preliminaries

Stochastic Liapunov Functions

Examples and the Perturbed Liapunov Function

MINIMUM VARIANCE CONTROL

Prediction

Control

Further illustrative examples

Relation to other control methods

Future prospects

LQ-STOCHASTIC CONTROL

LQ Regulation for Discrete Time Plants

Polynomial Approach

Reduced Complexity Regulators

The Servo Problem

LQ Stochastic Control of Continuous Time Plants

Relation to Other Approaches

DYNAMIC PROGRAMMING

An Example to Illustrate the Dynamic Programming Method

Finite Horizon Discrete Time Deterministic Systems

Finite Horizon Continuous Time Deterministic Systems

Time Varying Systems

Finite Horizon Discrete Time Stochastic Systems

Infinite Horizon Cost Functions

The Total Cost over an Infinite Horizon

The Discounted Cost Problem

The Average Cost Problem

Continuous Time Stochastic Systems

CONTROLLABILITY AND OBSERVABILITY OF DISTRIBUTED PARAMETER SYSTEMS

Controllability of infinite-dimensional systems

Controllability of distributed parameter systems

Observability

CONTROLLER DESIGN FOR DISTRIBUTED PARAMETER SYSTEMS

Control problems and control design methods

State space and semigroup approach

Internal model boundary control

Flatness-based approach

STATE ESTIMATION IN DISTRIBUTED PARAMETER SYSTEMS

State Estimation Problem

Optimal Estimation and Kalman Filtering

State Observers: Extension of Luenberger's Concept

TIME DELAY SYSTEMS

Examples of Delay Systems Derived from Distributed Parameter Systems

Controllability Notions for Linear Delay Systems

Quasi-finite Systems

An Example Stemming from the Wave Equation

GENERALISED MULTIDIMENTIONAL DISCRETE, CONTIUOUS-DISCRETE AND POSITIVE SYSTEMS

Models of generalised multidimensional linear systems.

Relationship between models.

Solutions to the 2-D models.

Singular 2-D continuous-discrete linear models.

Positive 2-D models.

Positive realization problem for 2-D Roesser model.

CONTROLLABILITY AND OBSERVABILITY OF 2D SYSTEMS

Unconstrained controllability

Singular systems

Constrained controllability

Positive systems

Continuous-discrete systems

Nonlinear systems

Observability

INDUSTRIAL APPLICATIONS OF 2D CONTROL SYSTEMS

Sheet Manufacturing Processes

2D models for sheet forming systems

2D ARMAX Estimation for Sheet Forming Systems

2D Controller Design for Sheet Forming Systems

Comparison of 2D Control of Sheet Forming Processes with Other Methods

Sensor and Gauges for 2D Industrial Processes

Concluding Remarks: 2D Actuation

STABILITY OF 2D SYSTEMS

Discrete Systems

Discrete-Continuous Systems

Continuous Systems

Applications

ANALYSIS OF NONLINEAR CONTROL SYSTEMS

Fundamental Properties

Sensitivity Analysis

The Small-gain Theorem

Passivity Theorems

Averaging

Singular Perturbations

Further Reading

LIE BRACKET

Basics of Manifolds and Bundles

Lie Derivatives and the Lie Bracket

Distributions and the Theorem of Frobenius

A Short Example

Concluding Remarks

DIFFERENTIAL GEOMETRIC APPROACH AND APPLICATION OF COMPUTER ALGEBRA

Remarks on Symbolic Computation

Some Mathematical Facts

Equivalence Problems

Some Applications

Concluding Remarks

VOLTERRA AND FLIESS SERIES EXPANSION

Functional representation of nonlinear systems

Recursive computation of the kernels.

Computation of the response to typical inputs

LYAPUNOV STABILITY

Autonomous Systems

The Invariance Principle

Linear Systems

Linearization

Non-autonomous Systems

Further Reading

INPUT-OUTPUT STABILITY

Signals and Norms

Systems and Gains

The Circle Theorem

Passivity

Interconnected Systems, Graphs and Robustness

Conclusions and Further Developments

CONTROLLABILITY AND OBSERVABILITY OF NONLINEAR SYSTEMS

Preliminaries

Controllability and accessibility

Observability

DESIGN FOR NONLINEAR CONTROL SYSTEMS

State-feedback design for global stability

State-feedback design for robust global stability

Semiglobal and practical stabilization

Output-feedback design

Conclusions

FEEDBACK LINEARIZATION OF NONLINEAR SYSTEMS

The problem of feedback linearization

Normal forms of single-input single-output systems

Conditions for exact linearization via feedback

NONLINEAR OUTPUT REGULATION

The problem of output regulation

Output regulation in the case of full information

Output regulation in the case of error feedback

Structurally stable regulation

NONLINEAR ZERO DYNAMICS IN CONTROL SYSTEMS

Nonlinear Control System Paradigms

Zero Dynamics in Control Systems

Nonminimum Phase Control Systems: Difficulties and Partial Solutions

Conclusion

FLATNESS BASED DESIGN

Equivalence and flatness

Feedback design with equivalence

Checking flatness: an overview

Concluding Remarks

LYAPUNOV DESIGN

Control Lyapunov Function

Lyapunov Design via Lyapunov Equation

Lyapunov Design for Matched and Unmatched Uncertainties

Property-based Lyapunov Design

Design Flexibilities and Considerations

Conclusions

Introduction

SLIDING MODE CONTROL

Concept “Sliding Mode”

Sliding Mode Equations

Existence Conditions

Design Principles

Discrete-Time Sliding Mode Control

Chattering Problem

Induction Motor Control

Conclusion

NONLINEAR OBSERVERS

Observability

Construction of Observers by Linear Approximation

Construction of Observers by Error Linearization

High Gain Observers

Nonlinear Filtering

Minimum Energy and H8 Estimation

Multiple Extended Kalman Filters

Conclusion

STATE RECONSTRUCTION IN NONLINEAR STOCHASTIC SYSTEMS BY EXTENDED KALMAN FILTER

The continuous-time extended Kalman filter

The discrete-time extended Kalman filter

PASSIVITY BASED CONTROL

Passivity: mathematically speaking

Stability of passive systems

PBC of Euler-Lagrange systems

Epilogue

CONTROL OF BIFURCATIONS

Bifurcation Control - The New Challenge

Bifurcations in Control Systems

Preliminaries of Bifurcation Theory

State-Feedback Control of Bifurcations

Some Other Bifurcation Control Methods

Controlling Multiple Limit Cycles

Potential Engineering Applications of Bifurcation Control

Future Research Outlook

ANALYSIS OF CHAOTIC SYSTEMS

Notion of chaos

Examples of chaotic systems

Criteria of chaos

Quantification of chaos

CONTROL OF CHAOTIC SYSTEMS

Notion of chaos

Models of controlled systems and control goals

Methods of controlling chaos: continuous-time systems

Discrete-time systems

Neural networks

Fuzzy systems

Control of chaos in distributed systems

Chaotic mixing

Generation of chaos (chaotization)

Other problems

Conclusions

DATA-BASED FUZZY MODELING

Process of Data-Based Modeling

Concepts for Fuzzy Modeling

Established Methods

Conclusion and Perspectives

OPTIMIZATION OF FUZZY CONTROLLERS

Basic principles of optimisation

Optimal Design of Fuzzy Controllers

Optimisation tools for fuzzy control

Applications

Conclusions

ANALYSIS AND STABILITY OF FUZZY SYSTEMS

Transformation Approaches

Stability Analysis

Further Tasks in the Analysis of Fuzzy Systems

Open Problems and Future Trends

Conclusions

FUZZY SYSTEM APPLICATIONS

Overview

Selected Examples

Conclusions

EXPERT CONTROL SYSTEMS: AN INTRODUCTION WITH CASE STUDIES

Expert control architecture

Knowledge representation in expert control

Knowledge acquisition in expert control

Reasoning in expert control

Real time expert systems

Expert systems in computer-aided control systems design

Anticipatory expert control

Case studies

Concluding Remarks

KNOWLEDGE-BASED AND LEARNING CONTROL SYSTEMS

General Concepts of Knowledge-Based and Learning Control Systems

Specific Features of the Knowledge-Based Control Systems

Relational and Logical Knowledge Representation

Statements and Solutions of Control Problems

Learning Processes in Knowledge-Based Control Systems

Descriptions of Initial Uncertainty

Related Problems

FUZZY EXPERT CONTROL SYSTEMS: KNOWLEDGE BASE VALIDATION

Integrated Control Systems

Fuzzy Expert Control System Methodology

Knowledge in Fuzzy Expert Control Systems

Main Design Issues

Objectives of Knowledge Validation for Control

Inference

Validation of Fuzzy Expert Controllers

Uncertain Models

Conclusions and Perspectives

BLACKBOARD ARCHITECTURE FOR INTELLIGENT CONTROL

Characteristics for Intelligent Control

Blackboard Architecture

Development of Blackboard Systems

The Structure of a Blackboard System

A Framework for Intelligent Control

Future Trends and Perspectives

MODELING OF DISCRETE EVENT SYSTEMS

Automata

Operations on Automata

Regular Languages and Finite-state Automata

Petri Nets

Process Algebras

Discussion on Timed Models

Introduction

SUPERVISORY CONTROL OF DISCRETE EVENT SYSTEMS

Control of Fully-Observed Discrete Event Systems

Control of Partially-Observed Discrete Event Systems

Avoiding Deadlock and Livelock

Controller Synthesis Techniques

Discussion

Introduction

SAMPLE PATH ANALYSIS OF DISCRETE EVENT DYNAMIC SYSTEMS (DEDS)

Perturbation Analysis

Markov Potential Theory Based Sample Path Sensitivity

Other Approaches: the Likelihhood Ratio Method

Sample Path Based Optimization

MODELING OF HYBRID SYSTEMS

Examples of Hybrid Systems

Mathematical Models for Hybrid Systems

Properties of Hybrid Systems

Software Tools

WELL-POSEDNESS OF HYBRID SYSTEMS

Model Classes

Solution Concepts

Well-posedness Notions

Well-posedness of Hybrid Automata

Well-posedness of Multi-modal linear Systems

Complementarity systems

Differential Equations with Discontinuous Right-hand Sides

STABILITY OF HYBRID SYSTEMS

Background and Motivation

Early Results

Stability via Multiple Lyapunov Functions

Further Results

BISIMULATIONS OF DISCRETE, CONTINUOUS, AND HYBRID SYSTEMS

Bisimulations of transition systems

Bisimulation of continuous systems

Bisimulations of hybrid systems

OPTIMAL CONTROL OF HYBRID SYSTEMS

Hybrid Dynamic Programming

Related Theory and Special Cases

VERIFICATION OF HYBRID SYSTEMS

Hybrid Model and Verification Methodology

Verifying Continuous Systems

Verifying Hybrid Systems

Flight Management System Example

STABILIZATION THROUGH HYBRID SYSTEMS

Switched Systems

Supervisors

Case Studies

CASE STUDY : AIR TRAFFIC MANAGEMENT SYSTEMS

A short History of Air Traffic Control

Organization of Air Traffic Control

Levels of Automation in the Current System

Conclusions

SUPERVISORY DISTRIBUTED COMPUTER CONTROL SYSTEMS

System and Component Structure

General System Services

Conclusions

FAULT DIAGNOSIS FOR LINEAR SYSTEMS

Model of the system, faults and uncertainties

Methods of residual generation

Parity space approach to residual generation

Observer-based residual generation

Fault analysis using parameter estimation

Residual evaluation

Conclusion and Perspectives

FAULT DIAGNOSIS FOR NONLINEAR SYSTEMS

Model Classes

Residual Generator Design

Fuzzy Model Based Fault Detection for Nonlinear Systems

Conclusion

DESIGN METHODS FOR ROBUST FAULT DIAGNOSIS

Model-based methods for FDI

Observer-based residual generation

The need for robustness in FDI

Robust FDI design using unknown input observers

Robust FDI design using eigenstructure assignment

Robust FDI design using H8 optimization

Concluding Remarks

QUALITATIVE METHODS FOR FAULT DIAGNOSIS

Basic properties of qualitative models

The diagnostic principle

Logic-based fault diagnosis

Diagnosis of discrete-event systems

Outlook

STATISTICAL METHODS FOR CHANGE DETECTION

Foundations-Detection

Foundations-Isolation

Case Studies-Vibrations

Introduction

INDUSTRIAL APPLICATIONS OF FAULT DIAGNOSIS

Methods

Application Examples

Future Aspects

Introduction and Overview

OFF-LINE METHODS FOR FAULT DIAGNOSIS AND INSPECTION

Parameter Estimation

Pattern Recognition for Fault Diagnosis

EXPERIENCE WITH KNOWLEDGE-BASED SYSTEMS FOR MAINTENANCE DIAGNOSIS

Development Steps in Methodology

Basic Characteristics of Early Fault Detection Methods

Condition Monitoring for Improved Maintenance in Nuclear Power Plants

Condition Monitoring for Improved Maintenance in Other Industries

Conclusions

FAULT TOLERANT SYSTEMS

Control and Fault Tolerant Control

Model Matching and the Pseudo-inverse Method

Optimal Control: the LQ problem

System reconfiguration and Structural Properties

Example

Conclusion

FAULT-TOLERANT CONTROL USING LMI DESIGN

Active Fault-Tolerant control Systems Design Using LMI Design

Fault Diagnostic Observer Design Using LMI Design for Uncertain Systems

Conclusion

STRUCTURAL ANALYSIS FOR FAULT DETECTION AND ISOLATION AND FOR FAULT TOLERANT CONTROL

Structural model

Matching on a bipartite graph

Causal interpretation

System Decomposition

Observability

Monitorability

Fault tolerant estimation

Controllability

A simple example

Conclusion

FAULT ACCOMODATION USING MODEL PREDICTIVE METHODS

The Fault Accommodation Problem

Failure Modeling

Failure Accommodation

Introduction

Conclusions

CONTROL RECONFIGURATION

Example

State of the Art

Reconfigurability Analysis

Reconfiguration Based on a Qualitative Model

Reconfiguration Based on Model-matching

Observer-based Control Reconfiguration

Reconfigurable Model-predictive Control

Outlook

ADAPTIVE AND NEURAL APPROACHES TO FAULT-TOLERANT CONTROL

An Adaptive Approach to Actuator Fault Tolerant Control

A Neural Network Approach to Sensor Fault Tolerant Control

A Neuro-Adaptive Approach to Process Fault Tolerant Control

Conclusions

STEAM GENERATORS AND STEAM DISTRIBUTION NETWORKS

Steam Generators

Steam Distribution Networks

Laws, Regulations, Guidelines, and Standards

Main Control Systems

Advanced Control Methods, Signal Processing, and Plant Management Systems.

Experience and Practical Suggestions

AUTOMATION AND CONTROL OF HVAC SYSTEMS

Conventional HVAC Control

Advanced HVAC Control

Conclusions

CONTROL OF SYNCHRONOUS GENERATORS

Voltage Control of Individual Synchronous Generators

Voltage Control with Electronic Power Converters

Excitation with Auxiliary Generators

Compounding

Indirect Generator Control

GAS TURBINES

Power Plant Setups

Gas Turbine Components

The Ideal Gas Turbine Cycle

Gas Turbine Control

Turbine Control System

AUTOMATION AND CONTROL OF ELECTRIC POWER GENERATION AND DISTRIBUTION SYSTEMS: STEAM TURBINES

Functional Specifications

Turbine Controller Design

Future Developments

AUTOMATIC CONTROL FOR HYDROELECTRIC POWER PLANTS

Safety Systems for Hydropower Units

Standard Control Algorithms

Implementation issues

Advanced Control Features

Outlook: Driving Forces for Further Development

ELECTRICAL NETWORK CONTROL

Power system engineering

Evolution of electrical network control technology

System engineering aspects

Typical control center functions

COMBINED CYCLE AND COMBINED HEAT AND POWER PROCESSES

Elements of Combined Cycle / Combined Heat and Power Processes

Typical CC/CHP Configurations

Operation of CC/CHP Plants

Automatic Control in CC/CHP Plants

Control Philosophy in Future Combined Cycle Power Plants

Conclusions

Introduction

CONTROL OF LARGE NUCLEAR REACTORS BY STATE AND OUTPUT FEEDBACK TECHNIQUES

On certain Preliminaries on Nuclear Reactor

Modeling of Nuclear Reactors

Control of Nuclear Reactor

Application to a large Pressurized Heavy Water Reactor

Conclusion

AUTOMATION AND CONTROL IN IRON AND STEEL INDUSTRIES

Overview of Processes in Integrated Steelworks

Control of Metallurgical Processes

Control of Rolling Processes and Processing Lines

Overall Automation Systems

Development Trends

AUTOMATION AND CONTROL OF CHEMICAL AND PETROCHEMICAL PLANTS

The Chemical and Petrochemical Industries

Historical Perspective

Overview of Industrial Process Control

Traditional Process Control Strategies

Control System Design

Advanced Control Techniques

AUTOMATION AND CONTROL IN CEMENT INDUSTRIES

Description of the Technology

Control Problems and Systems

Control Systems Technology

Application of the Advanced Control Theory

AUTOMATION AND CONTROL OF PULP AND PAPER PROCESSES

Pulping Processes

Paper Mill

Control of Mechanical Pulp Making

Control of Chemical Pulp Making

Control of Papermaking

Future Control Issues

AUTOMATION IN WASTEWATER TREATMENT

The urban water system

Wastewater Treatment Operation

Incentives for Automation

Automation today in Wastewater treatment

Modeling wastewater treatment

Automation components

Discussion

MODELING AND CONTROL OF COMPLEX RIVER AND WATER RESERVOIR SYSTEMS

Models of water plants

Control strategies for water plants

Conclusion

AUTOMATION AND CONTROL IN FOOD PRODUCTION

Automation of Food Production on the Processing Oriented Levels

Future Trends in the Automation of Food Production Processes

Introduction

MACHINE TOOL

Machine tool Monitoring and Control

Machine Monitoring and Tool Inspection

Outlook

WELDING

Model Building of welding Process

Welding Control

Welding Sensors

Welding Robots

Monitoring and Inspection

Future Trends

AUTOMATION AND CONTROL IN ELECTRONIC INDUSTRIES

Design and Test Automation

Automated Test Equipment

Semiconductor Manufacturing

Automated Visual Inspection

Packages and Interconnections

Automated Assembly

Future Trends

ADVANCED TECHNOLOGIES AND AUTOMATION IN AGRICULTURE

Examples of Advanced Precision Agriculture Components: Combine Harvester, Sprayer, Fertilizer Spreader

Networks in Agriculture

AUTOMATION IN FISHERIES AND AQUACULTURE TECHNOLOGY

Traditional harvesting technology. Relations to instrumentation, mechanization, control and automation.

New harvesting concepts.

Processing of fish and other products.

Aquaculture. Relations to automation and control.

AUTOMOTIVE CONTROL SYSTEMS

Potential of Alternate Fuels and Propulsion Systems

Basic Engine Operation

Lambda Control

Idle Speed Control

Knock Control in SI Engines

Vehicle Modeling

ABS Control Systems

Yaw Dynamic Control

INTELLIGENT CONTROL OF ROAD VEHICLES FOR AUTOMATED DRIVING: PATH ARCHITECTURE FOR AUTOMATED HIGHWAY SYSTEMS AND LATERAL GUIDANCE

AHS Architecture

Vehicle Models for Lateral Control

Road Reference System

Lateral Controllers for AHS

Modeling and Lateral Control of Heavy Duty Vehicles

Concluding Remarks

SHIP STEERING

Modeling

Automatic Steering

Review of the Different Controller Strategies for Different Classes of Ships

Introduction

Conclusions, Future Developments, and Further Reading

CONTROL FOR RAILWAY VEHICLES

Overview of Railway Vehicle, Vehicle Models and Track Inputs

Traction and Braking Control Systems

Pantograph Control

Suspension and Guidance

Conclusion and Trends

TRAIN AND RAILWAY OPERATIONS CONTROL

Control system overview

Single train control

Multiple train control and protection on a single track

Multiple train on multiple track (Network control)

AEROSPACE

Control of Aeronautical Vehicles

Aircraft Flight Control Systems

The Principles of Flight Control

Primary Flying Controls

AFCS Modes

Fly-by-Wire and "Fly-by-Light" Systems

Flight Control Functions

Future Flight Control Systems

Conclusions

SENSORS IN CONTROL SYSTEMS

Sensor Fundamentals and Classifications

Sensors in Control Systems

Sensor Technology Developments

Biological Systems, Chemical sensors, and Biosensors

Sensor-Enabled Visions for the Future

SELF-SENSING SOLID-STATE ACTUATORS

Solid-state transducers

Measurement and power electronics

Compensation and reconstruction filters

Application example - piezoelectric micropositioning system with one degree of freedom

Conclusions

BUS SYSTEMS

General Reflections

Parallel Bus Systems

Serial Bus Systems

PROGRAMMABLE LOGIC CONTROLLERS

Historical Aspects

PLC Programming Languages

Professional Practice

Future Trends and Perspectives

COMPUTER-AIDED CONTROL SYSTEM ENGINEERING TOOLS

CAD techniques

Trends

Introduction

HUMAN-MACHINE INTERACTION

Human Tasks with Automation and Control

Human-Machine Interfaces

Knowledge-Based Support

Design and Evaluation

Conclusions

CONTROL OF ELECTRICAL MACHINES FOR DRIVES

General Remarks on Electrical Machines

DC Drives

DC-Power Amplifier

Speed Control of DC Machines

Vector Representation for the Quantities of AC Machines

The Two-Axis Machine Model

The Park Transformation

AC-Power Converter

Vector Control of AC Machines

ROBOT KINEMATICS AND DYNAMICS

Kinematics

Dynamics

Dynamic Parameter Identification

Symbolic Modelling

TRAJECTORY AND TASK PLANNING

Path Planning for Mobile Robots

Trajectory Planning of Robot Manipulators

Task Planning

Optimization Methods for Motion Planning

Concluding Remarks

ROBOT CONTROL AND PROGRAMMING

Robot Dynamics

Motion Control

Force Control

Robot Programming

INTELLIGENT ROBOTS

Fuzzy Computing

Neural Computing

Evolutionary Computing

Reinforcement Learning

Intelligence on Robotics

Concluding Remarks

ROBOTIC APPLICATIONS TO LIFE SUPPORT SYSTEMS

Basic Technologies

Intelligent Wheelchair Control

Intelligent Manipulator Control

Programing by Demonstration

Agent Orientated Software Design