Most existing robust design books address design for static systems, or achieve robust design from experimental data via the Taguchi method. Little work considers model information for robust design particularly for the dynamic system. This book covers robust design for both static and dynamic systems using the nominal model information or the hybrid model/data information, and also integrates design with control under a large operating region. This design can handle strong nonlinearity and more uncertainties from model and parameters.

Han-Xiong Li is a professor in the Department of Systems Engineering at the City University of Hong Kong. Dr. Li serves as Associate Editor of IEEE Transaction on Cybernetics, and IEEE Transactions on Industrial Electronics. Over the last thirty years, he has worked in different fields, including military service, industry, and academia. His current research interests are in systems intelligence and control, process design and control integration, and distributed parameter systems with applications to electronics packaging.

XinJiang Lu is currently an associate professor with the School of Mechanical and Electrical Engineering at Central South University, People's Republic of China. He was awarded the Hiwin Doctoral Dissertation Award in 2011 and the New Century Excellent Talents Award by the Chinese Ministry of Education in 2013. His research interests include robust design, integration of design and control, and process modeling and control.

PREFACE xi

ACKNOWLEDGMENTS xiii

I BACKGROUND AND FUNDAMENTALS

1 INTRODUCTION 3

1.1 Background and Motivation 3

1.1.1 Robust Design for Static Systems 5

1.1.2 Robust Design for Dynamic Systems 8

1.1.3 Integration of Design and Control 10

1.2 Objectives of the Book 14

1.3 Contribution and Organization of the Book 15

2 OVERVIEW AND CLASSIFICATION 19

2.1 Classification of Uncertainty 19

2.2 Robust Performance Analysis 20

2.2.1 Interval Analysis 20

2.2.2 Fuzzy Analysis 21

2.2.3 Probabilistic Analysis 21

2.3 Robust Design 27

2.3.1 Robust Design for Static Systems 28

2.3.2 Robust Design for Dynamic Systems 37

2.4 Integration of Design and Control 41

2.4.1 Control Structure Design 41

2.4.2 Control Method 42

2.4.3 Optimization Method 43

2.5 Problems and Research Opportunities 43

II ROBUST DESIGN FOR STATIC SYSTEMS

3 VARIABLE SENSITIVITY BASED ROBUST DESIGN FOR NONLINEAR SYSTEM 47

3.1 Introduction 47

3.2 Design Problem for Nonlinear Systems 48

3.2.1 Problem in Deterministic Design 49

3.2.2 Problem in Probabilistic Design 49

3.3 Concept of Variable Sensitivity 51

3.4 Variable Sensitivity Based Deterministic Robust Design 52

3.4.1 Robust Design for Single Performance Single Variable 52

3.4.2 Robust Design for Multiperformances Multivariables 54

3.4.3 Design Procedure 58

3.5 Variable Sensitivity Based Probabilistic Robust Design 58

3.5.1 Single Performance Function Under Single Variables 59

3.5.2 Single Performance Function Under Multivariables 60

3.5.3 Multiperformance Functions Under Multivariables 61

3.6 Case Study 62

3.6.1 Deterministic Design Cases 62

3.6.2 Probabilistic Design Case 66

3.7 Summary 70

4 MULTI-DOMAIN MODELING-BASED ROBUST DESIGN 71

4.1 Introduction 71

4.2 Multi-Domain Modeling-Based Robust Design Methodology 73

4.2.1 Multi-Domain Modeling Approach 74

4.2.2 Variation Separation-Based Robust Design Method 75

4.2.3 Design Procedure 78

4.3 Case Study 80

4.3.1 Robust Design of a Belt 80

4.3.2 Robust Design of Hydraulic Press Machine 81

4.4 Summary 86

5 HYBRID MODEL DATA-BASED ROBUST DESIGN UNDER MODEL UNCERTAINTY 87

5.1 Introduction 87

5.2 Design Problem for Partially Unknown Systems 88

5.2.1 Probabilistic Robust Design Problem 88

5.2.2 Deterministic Robust Design Problem 90

5.3 Hybrid Model Data-Based Robust Design Methodology 92

5.3.1 Probabilistic Robust Design 93

5.3.2 Deterministic Robust Design 99

5.4 Case Study 104

5.4.1 Probabilistic Robust Design 104

5.4.2 Deterministic Robust Design 109

5.5 Summary 114

III ROBUST DESIGN FOR DYNAMIC SYSTEMS

6 ROBUST EIGENVALUE DESIGN UNDER PARAMETER VARIATIONA LINEARIZATION APPROACH 119

6.1 Introduction 119

6.2 Dynamic Design Problem Under Parameter Variation 120

6.2.1 Stability Design Problem 120

6.2.2 Dynamic Robust Design Problem 121

6.3 Linearization-Based Robust Eigenvalue Design 122

6.3.1 Stability Design 122

6.3.2 Robust Eigenvalue Design 124

6.3.3 Tolerance Design 127

6.3.4 Design Procedure 128

6.4 Multi-Model-Based Robust Design Method for Stability and Robustness 128

6.4.1 Multi-Model Approach 129

6.4.2 Stability Design 130

6.4.3 Dynamic Robust Design 132

6.4.4 Summary 134

6.5 Case Studies 134

6.5.1 Linearization-Based Robust Eigenvalue Design 134

6.5.2 Multi-Model-Based Robust Design Method 138

6.6 Summary ...