| Titre : | Multivariable Control Systems : an engineering approach | | Type de document : | texte imprimé | | Auteurs : | P. Albertos, Auteur ; A. Sala, Auteur | | Editeur : | London : Springer-Verlag | | Année de publication : | 2004 | | ISBN/ISSN/EAN : | 978-1-85233-738-4 | | Langues : | Anglais (eng) | | Index. décimale : | 25-07 Théorie de la commande: commandes des processus | | Résumé : | Multivariable control techniques solve issues of complex specification and modelling errors elegantly but the complexity of the underlying mathematics is much higher than presented in traditional single-input, single-output control courses
Multivariable Control Systems focuses on control design with continual references to the practical aspects of implementation. While the concepts of multivariable control are justified, the book emphasises the need to maintain student interest and motivation over exhaustively rigorous mathematical proof. Tools of analysis and representation are always developed as methods for achieving a final control system design and evaluation.
Features:
- design implementation clearly laid out using extensive reference to MATLAB(R);
- combined consideration of systems (plant) and signals (mainly disturbances) in a fluent but simple presentation;
- step-by-step approach from the objectives of multivariable control to the solution of complete design problems.
Multivariable Control Systems is an ideal text for masters students, students beginning their Ph.D. or for final-year undergraduates looking for more depth than provided by introductory textbooks. It will also interest the control engineer practising in industry and seeking to implement robust or multivariable control solutions to plant problems in as straightforward a manner as possible. | | Note de contenu : | Contents
1 Introduction to Multivariable Control
1.1 Introduction
1.2 Process and Instrumentation
1.3 Process Variables
1.4 The Process Behaviour
1.5 Control Aims
1.6 Modes of Operation
1.7 The Need for Feedback
1.8 Model-free vs. Model-based Control
1.9 The Importance of Considering Modelling Errors
1.10 Multivariable Systems
1.11 Implementation and Structural Issues
2 Linear System Representation: Models and Equivalence
2.1 Introduction: Objectives of Modelling
2.2 Types of Models.
2.3 First-principle Models: Components
2.4 Internal Representation: State Variables
2.5 Linear Models and Linearisation
2.6 Input/Output Representations
2.7 Systems and Subsystems: Interconnection
2.8 Discretised Models.
2.9 Equivalence of Representations
2.10 Disturbance Models
2.11 Key Issues in Modelling
2.12 Case Study: The Paper Machine Headbox
3 Linear Systems Analysis
3.1 Introduction
3.2 Linear System Time-response
3.3 Stability Conditions
3.4 Discretisation
3.5 Gain
3.6 Frequency response
3.7 System Internal Structure
3.8 Block System Structure (Kalman Form)
3.9 Input/Output Properties
3.10 Model Reduction
3.11 Key Issues in MIMO Systems Analysis
3.12 Case Study: Simple Distillation Column
4 Solutions to the Control Problem
4.1 The Control Design Problem
4.2 Control Goals
4.3 Variables Selection
4.4 Control Structures
4.5 Feedback Control
4.6 Feedforward Control
4.7 Two Degree of Freedom Controller
4.8 Hierarchical Control
4.9 Key Issues in Control Design.
4.10 Case Study: Ceramic Kiln
5 Decentralised and Decoupled Control
5.1 Introduction
5.2 Multi-loop Control, Pairing Selection
5.4 Enhancing SISO Loops with MIMO Techniques: Cascade Control
5.5 Other Possibilities
5.6 Sequential-Hierarchical Design and Tuning
5.7 Key Conclusions
5.8 Case Studies
6 Fundamentals of Centralised Closed-loop Control
6.1 State Feedback
6.2 Output Feedback
6.3 Rejection of Deterministic Unmeasurable Disturbances
7 Optimisation-based control
7.1 optimal state feedback
7.2 optimal output feedback
7.3 predictive control
7.4 a generalised optimal disturbance-rejection problem
8 Designing for robustness
8.1 the downside of model-based control
2.2 uncertainty and feedback
8.3 limitations in achievable performance due to uncertainty
8.4 trade-offs and design guidelines
8.5 robustness analysis methodologies
8.6 controller synthesis
9 Implementation and other issues
9.1 control implementation: centralised vs. deceentralised
9.2 implementation technologies
9.3 bumpless transfer and anti-windup
9.4 non-conventational sampling
9.5 coping with non-linearity
9.6 reliability and faut detection
9.7 supervision,integrated automation,plant-wide control
A Summary of SISO system analysis
B Matrices
C Signal and system norms
D Optimisation
E Multivariable statistics
F Robust control analysis and synthesis |
Multivariable Control Systems : an engineering approach [texte imprimé] / P. Albertos, Auteur ; A. Sala, Auteur . - London : Springer-Verlag, 2004. ISBN : 978-1-85233-738-4 Langues : Anglais ( eng) | Index. décimale : | 25-07 Théorie de la commande: commandes des processus | | Résumé : | Multivariable control techniques solve issues of complex specification and modelling errors elegantly but the complexity of the underlying mathematics is much higher than presented in traditional single-input, single-output control courses
Multivariable Control Systems focuses on control design with continual references to the practical aspects of implementation. While the concepts of multivariable control are justified, the book emphasises the need to maintain student interest and motivation over exhaustively rigorous mathematical proof. Tools of analysis and representation are always developed as methods for achieving a final control system design and evaluation.
Features:
- design implementation clearly laid out using extensive reference to MATLAB(R);
- combined consideration of systems (plant) and signals (mainly disturbances) in a fluent but simple presentation;
- step-by-step approach from the objectives of multivariable control to the solution of complete design problems.
Multivariable Control Systems is an ideal text for masters students, students beginning their Ph.D. or for final-year undergraduates looking for more depth than provided by introductory textbooks. It will also interest the control engineer practising in industry and seeking to implement robust or multivariable control solutions to plant problems in as straightforward a manner as possible. | | Note de contenu : | Contents
1 Introduction to Multivariable Control
1.1 Introduction
1.2 Process and Instrumentation
1.3 Process Variables
1.4 The Process Behaviour
1.5 Control Aims
1.6 Modes of Operation
1.7 The Need for Feedback
1.8 Model-free vs. Model-based Control
1.9 The Importance of Considering Modelling Errors
1.10 Multivariable Systems
1.11 Implementation and Structural Issues
2 Linear System Representation: Models and Equivalence
2.1 Introduction: Objectives of Modelling
2.2 Types of Models.
2.3 First-principle Models: Components
2.4 Internal Representation: State Variables
2.5 Linear Models and Linearisation
2.6 Input/Output Representations
2.7 Systems and Subsystems: Interconnection
2.8 Discretised Models.
2.9 Equivalence of Representations
2.10 Disturbance Models
2.11 Key Issues in Modelling
2.12 Case Study: The Paper Machine Headbox
3 Linear Systems Analysis
3.1 Introduction
3.2 Linear System Time-response
3.3 Stability Conditions
3.4 Discretisation
3.5 Gain
3.6 Frequency response
3.7 System Internal Structure
3.8 Block System Structure (Kalman Form)
3.9 Input/Output Properties
3.10 Model Reduction
3.11 Key Issues in MIMO Systems Analysis
3.12 Case Study: Simple Distillation Column
4 Solutions to the Control Problem
4.1 The Control Design Problem
4.2 Control Goals
4.3 Variables Selection
4.4 Control Structures
4.5 Feedback Control
4.6 Feedforward Control
4.7 Two Degree of Freedom Controller
4.8 Hierarchical Control
4.9 Key Issues in Control Design.
4.10 Case Study: Ceramic Kiln
5 Decentralised and Decoupled Control
5.1 Introduction
5.2 Multi-loop Control, Pairing Selection
5.4 Enhancing SISO Loops with MIMO Techniques: Cascade Control
5.5 Other Possibilities
5.6 Sequential-Hierarchical Design and Tuning
5.7 Key Conclusions
5.8 Case Studies
6 Fundamentals of Centralised Closed-loop Control
6.1 State Feedback
6.2 Output Feedback
6.3 Rejection of Deterministic Unmeasurable Disturbances
7 Optimisation-based control
7.1 optimal state feedback
7.2 optimal output feedback
7.3 predictive control
7.4 a generalised optimal disturbance-rejection problem
8 Designing for robustness
8.1 the downside of model-based control
2.2 uncertainty and feedback
8.3 limitations in achievable performance due to uncertainty
8.4 trade-offs and design guidelines
8.5 robustness analysis methodologies
8.6 controller synthesis
9 Implementation and other issues
9.1 control implementation: centralised vs. deceentralised
9.2 implementation technologies
9.3 bumpless transfer and anti-windup
9.4 non-conventational sampling
9.5 coping with non-linearity
9.6 reliability and faut detection
9.7 supervision,integrated automation,plant-wide control
A Summary of SISO system analysis
B Matrices
C Signal and system norms
D Optimisation
E Multivariable statistics
F Robust control analysis and synthesis |
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Multivariable Control Systems / P. Albertos
