内容简介：　　　

　　This book is intended to cover the central concepts of multiobjective optimisation and control techniques. It explains the fundamental theory along with a number of design methods and algorithms. In addition, applications of multiobjective optimisation and control are presented by reporting on leading recent research work on this subject. It is of interest specifically to students who have a technical background in control engineering and engineering mathematics. This hardback edition can be used either as a reference manual by professionals or as a course text at the undergraduate or postgraduate level. The book should also be useful to control system designers and researchers and other specialists from the host of disciplines from which practical optimisation and control applications are drawn.

英文目录：
Preface
Symbols and Abbreviations
1 Introduction
　　1.1 Multiobjective Optimisation
　　　　1.1.1 Constrained Optimisation
　　　　1.1.2 Conventional Multiobjective Optimisation
　　　　1.1.3 Method of Inequalities
　　　　1.1.4 Mutiobjective Genetic Algorithms
　　1.2 Multiobjective Control
　　　　1.2.1 Conflicts and Trade offs in Control Systems
　　　　1.2.2 Multiobjective Robust Control
　　　　1.2.3 Multiobjective Critical Control
　　　　1.2.4 Multiobjective Eigenstructure Assignment
　　　　1.2.5 Multiobjective PID Control
　　　　1.2.6 Multiobjective Optimisation of Control Implementations
　　　　1.2.7 Multiobjective Nonlinear Identification
　　　　1.2.8 Multiobjective Fault Detection
　　1.3 Outline of the Book
2 Nonlinear Optimisation
　　2.1 One Dimensional Optimisation
　　　　2.1.1 The Dichotomy Method with Derivatives
　　　　2.1.2 The Dichotomy Method without Derivatives
　　　　2.1.3 The Fibonacci Method
　　　　2.1.4 The Golden Section Search Meted
　　2.2 Optimisation Conditions
　　　　2.2.1 Necessary Conditions for Local Optimality
　　　　2.2.2 Sufficient Conditions for Local Optimality
　　2.3 Unconstrained Optimisation Methods
　　　　2.3.1 Steepest Decent Method
　　　　2.3.2 Newton s Method
　　　　2.3.3 Quasi Newton s Methods
　　2.4 Summary
3 Constrained Optimisation
　　3.1 Introduction
　　3.2 Optimality Conditions
　　　　3.2.1 Basic Concepts
　　　　3.2.2 Kutn Tucker Necessary Condition
　　　　3.2.3 Second Order Sufficient Conditions
　　3.3 Primal Methods
　　　　3.3.1 Sequential Linear Programming
　　　　3.3.2 Sequential Quadratic Programming
　　3.4 Dual Methods
　　　　3.4.1 Lagrangean Methods
　　　　3.4.2 Method of Exterior Penalties
　　　　3.4.3 Method of Interior Penalties
　　3.5 Summary
4 Multiple Objective Optimisation
　　4.1 Introduction
　　4.2 Basic Concepts and Methods
　　　　4.2.1 Concepts and Definitions
　　　　4.2.2 Method Classification
　　　　4.2.3 Simple Weighting Method
　　4.3 Norm Methods
　　　　4.3.1 Minimax (Ideal Point) Method
　　　　4.3.2 Goal Attainment Method
　　　　4.3.3 Goal Programming
　　　　4.3.4 The Minimax Reference Point Method
　　4.4 Interactive Methods
　　　　4.4.1 Geoffrions Method
　　　　4.4.2 The STEM Method
　　　　4.4.3 The ISTM Method
　　　　4.4.4 The Gradient Projection Method
　　4.5 Summary
5 Genetic Algorithms and Optimisation
　　5.1 Introduction
　　5.2 What are Genetic Algorithms
　　5.3 Basic Structure of Genetic Algorithms
　　5.4 Population Representation and Initialisation
　　　　5.4.1 Binary Representation
　　　　5.4.2 Real Valued Representation
　　　　5.4.3 Initialisation
　　5.5 Fitness Functions
　　5.6 Selection
　　　　5.6.1 Roulette Wheel Selection Methods
　　　　5.6.2 Stochastic Universal Sampling
　　5.7 Crossover
　　　　5.7.1 Single Point Crossover
　　　　5.7.2 Multi Point Crossover
　　　　5.7.3 Uniform Crossover
　　　　5.7.4 Other Crossover Operators
　　　　5.7.5 Intermediate Recombination
　　　　5.7.6 Line Recombination
　　5.8 Mutation
　　5.9 Reinsertion and Termination
　　　　5.9.1 Reinsertion
　　　　5.9.2 Termination
　　5.10 Multiobjective Optimisation with GAs
　　　　5.10.1 Constrained Optimasation
　　　　5.10.2 Non Pareto Optimisation
　　　　5.10.3 Pareto Based Optimisation
　　5.11 An Example
　　5.12 Summary
6 Robust Control System Design by Mixed Optimisation
　　6.1 Introduction
　　6.2 An H∞ Loop Shaping Design Procedure
　　　　6.2.1 Overview
　　　　6.2.2 Preliminaries
　　　　6.2.3 Normalised Left Coprime Factisntion
　　　　6.2.4 Coprime Factor Robust H∞ Stability Problem
　　　　6.2.5 A Loop Shaping Design Procedure (LSDP)
　　　　6.2.6 Example The Inverted Pendulum
　　6.3 Mixed OPtimisation for the LSDP
　　　　6.3.1 MATLAB Implementation Thr MODCONS Toolbox
　　6.4 Example The Distillation Column
　　6.5 Example High Speed EMS Maglev Vehicle
　　6.6 Summary
7 Multiobjective Control of Critical Systems
　　7.1 Introduction
　　7.2 Critical Control Systems
　　7.3 Critical System Descriptions
　　7.4 Input Spaces of Systems
　　7.5 Multiobjective Critical Control
　　7.6 Control Design of SISO Critical Systems
　　7.7 Control Design of MIMO Critical Systems
　　7.8 An Example
　　7.9 Summary
8 Multiobjective Control Using Eigenstructure Assignment
　　8.1 Introduction
　　8.2 What is Eigenstructure Assignment
　　8.3 Allowable Eigenvector Subspaces
　　8.4 Parametric Eigenstructure Assignment
　　8.5 Multiobjective Eigenstructure Assignment
　　8.6 Controller Design Using the Method of Inequalities
　　8.7 Controller Design Using Genetic Algorithms
　　8.8 Summary
9 Multiobjective PI Controller Design for a Gasifler
　　9.1 Introduction
　　9.2 Modeling of the Gasifler
　　9.3 System Specifications of the Gasifler
　　9.4 Multiobjective PI Control Fromulation
　　9.5 Multiobjective Optimal Tunning PI Control
　　9.6 Simulation Results and Discussions
　　9.7 Summary
10 Multiobjective PID Controller Implement at ion Design
　　10.1 Introduction
　　10.2 FWL Fixed Point Representation
　　　　10.2.1 A Linear System Equivalence Completion Problem
　　10.3 MOGA for Optimal FWL Controller Structures
　　　　10.3.1 Multiobjective Genetic Algorithm
　　　　10.3.2 Procedure Outline
　　　　10.3.3 Encoding of Solution Space
　　10.4 Example Steel Rolling Mill System
　　　　10.4.1 Performance indices
　　　　10.4.2 Nominal Plant Model
　　　　10.4.3 Controller
　　　　10.4.4 Design Results
　　10.5 Example IFAC93 Benchmark Design
　　　　10.5.1 Performance Indices
　　　　10.5.2 Nonminal Plant Model and Controller
　　　　10.5.3 Design Results
　　10.6 Summary
11 Multiobjective Nonlinear Identiflcation
　　11.1 Introduction
　　11.2 Nrural Networks
　　11.3 Gaussian Radial Basis Function Networks
　　11.4 Nonlinear Modelling with Neural Networks
　　11.5 Modelling Selection by Genetic Algorithms
　　11.6 Multiobjective Identiflcation Criteria
　　11.7 Multiobjective Identiflcation Algorithm
　　11.8 Example
　　　　11.8.1 Example 1
　　　　11.8.2 Example 2
　　11.9 Summary
12 Multiobjective Fault Diagnosis
　　12.1 Introduction
　　12.2 Overview of Robust Fault Diagnosis
　　12.3 Observer Based Fault Diagnosis
　　12.4 Multiple Objectives of Fault Diagnosis
　　12.5 Disturbance Distribution and Fault Isolation
　　12.6 Paramenterisation of Fault Diagnosis
　　12.7 Multiobjective Fault Diagnosis
　　12.8 An Example
　　12.9 Summary
Bibliography
Index