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E-grāmata: System Design and Control Integration for Advanced Manufacturing

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As well as reviewing recent research on the robust design of advanced manufacturing systems and its integration with control, Li and Lu also develop new designs and integration methods to tackle some unsolved problems in the advanced manufacturing. They begin with a systematic overview and classification of robust analysis and design for static and dynamic systems and the integration of design and control. Other topics include variable sensitivity based robust design for nonlinear systems, hybrid model/data-based robust design under model uncertainty, a nonlinear approach to robust eigenvalue design under parameter variation, and intelligence-based hybrid integration. Annotation ©2015 Ringgold, Inc., Portland, OR (protoview.com)

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.

Preface xi
Acknowledgments xiii
I Background And Fundamentals
1 Introduction
3(16)
1.1 Background and Motivation
3(11)
1.1.1 Robust Design for Static Systems
5(3)
1.1.2 Robust Design for Dynamic Systems
8(2)
1.1.3 Integration of Design and Control
10(4)
1.2 Objectives of the Book
14(1)
1.3 Contribution and Organization of the Book
15(4)
2 Overview And Classification
19(28)
2.1 Classification of Uncertainty
19(1)
2.2 Robust Performance Analysis
20(7)
2.2.1 Interval Analysis
20(1)
2.2.2 Fuzzy Analysis
21(1)
2.2.3 Probabilistic Analysis
21(6)
2.3 Robust Design
27(14)
2.3.1 Robust Design for Static Systems
28(9)
2.3.2 Robust Design for Dynamic Systems
37(4)
2.4 Integration of Design and Control
41(2)
2.4.1 Control Structure Design
41(1)
2.4.2 Control Method
42(1)
2.4.3 Optimization Method
43(1)
2.5 Problems and Research Opportunities
43(4)
II Robust Design For Static Systems
3 Variable Sensitivity Based Robust Design For Nonlinear System
47(24)
3.1 Introduction
47(1)
3.2 Design Problem for Nonlinear Systems
48(3)
3.2.1 Problem in Deterministic Design
49(1)
3.2.2 Problem in Probabilistic Design
49(2)
3.3 Concept of Variable Sensitivity
51(1)
3.4 Variable Sensitivity Based Deterministic Robust Design
52(6)
3.4.1 Robust Design for Single Performance Single Variable
52(2)
3.4.2 Robust Design for Multiperformances Multivariables
54(4)
3.4.3 Design Procedure
58(1)
3.5 Variable Sensitivity Based Probabilistic Robust Design
58(4)
3.5.1 Single Performance Function Under Single Variables
59(1)
3.5.2 Single Performance Function Under Multivariables
60(1)
3.5.3 Multiperformance Functions Under Multivariables
61(1)
3.6 Case Study
62(8)
3.6.1 Deterministic Design Cases
62(4)
3.6.2 Probabilistic Design Case
66(4)
3.7 Summary
70(1)
4 Multi-Domain Modeling-Based Robust Design
71(16)
4.1 Introduction
71(2)
4.2 Multi-Domain Modeling-Based Robust Design Methodology
73(7)
4.2.1 Multi-Domain Modeling Approach
74(1)
4.2.2 Variation Separation-Based Robust Design Method
75(3)
4.2.3 Design Procedure
78(2)
4.3 Case Study
80(6)
4.3.1 Robust Design of a Belt
80(1)
4.3.2 Robust Design of Hydraulic Press Machine
81(5)
4.4 Summary
86(1)
5 Hybrid Model Data-Based Robust Design Under Model Uncertainty
87(32)
5.1 Introduction
87(1)
5.2 Design Problem for Partially Unknown Systems
88(4)
5.2.1 Probabilistic Robust Design Problem
88(2)
5.2.2 Deterministic Robust Design Problem
90(2)
5.3 Hybrid Model Data-Based Robust Design Methodology
92(12)
5.3.1 Probabilistic Robust Design
93(6)
5.3.2 Deterministic Robust Design
99(5)
5.4 Case Study
104(10)
5.4.1 Probabilistic Robust Design
104(5)
5.4.2 Deterministic Robust Design
109(5)
5.5 Summary
114(5)
III Robust Design For Dynamic Systems
6 Robust Eigenvalue Design Under Parameter Variation-A Linearization Approach
119(28)
6.1 Introduction
119(1)
6.2 Dynamic Design Problem Under Parameter Variation
120(2)
6.2.1 Stability Design Problem
120(1)
6.2.2 Dynamic Robust Design Problem
121(1)
6.3 Linearization-Based Robust Eigenvalue Design
122(6)
6.3.1 Stability Design
122(2)
6.3.2 Robust Eigenvalue Design
124(3)
6.3.3 Tolerance Design
127(1)
6.3.4 Design Procedure
128(1)
6.4 Multi-Model-Based Robust Design Method for Stability and Robustness
128(6)
6.4.1 Multi-Model Approach
129(1)
6.4.2 Stability Design
130(2)
6.4.3 Dynamic Robust Design
132(2)
6.4.4 Summary
134(1)
6.5 Case Studies
134(11)
6.5.1 Linearization-Based Robust Eigenvalue Design
134(4)
6.5.2 Multi-Model-Based Robust Design Method
138(7)
6.6 Summary
145(2)
7 Robust Eigenvalue Design Under Parameter Variation-A Nonlinear Approach
147(20)
7.1 Introduction
147(1)
7.2 Design Problem
148(2)
7.3 SN-Based Dynamic Design
150(10)
7.3.1 Stability Design
152(1)
7.3.2 Dynamic Robust Design
153(7)
7.4 Case Study
160(5)
7.4.1 Stability Design
160(2)
7.4.2 Dynamic Robust Design
162(3)
7.5 Summary
165(2)
8 Robust Eigenvalue Design Under Model Uncertainty
167(16)
8.1 Introduction
167(1)
8.2 Design Problem for Partially Unknown Dynamic Systems
168(1)
8.3 Stability Design
169(3)
8.3.1 Stability Design for Nominal Model
169(1)
8.3.2 Stability Design Under Model Uncertainty
169(2)
8.3.3 Stability Bound of Design Variables
171(1)
8.4 Robust Eigenvalue Design and Tolerance Design
172(3)
8.4.1 Robust Eigenvalue Design
172(1)
8.4.2 Tolerance Design
173(1)
8.4.3 Design Procedure
174(1)
8.5 Case Study
175(5)
8.5.1 Design of the Nominal Stability Space
175(1)
8.5.2 Design of the Stability Space
176(1)
8.5.3 Design of the Robust Stability Space
176(1)
8.5.4 Robust Eigenvalue Design
176(1)
8.5.5 Tolerance Design
177(1)
8.5.6 Design Verification
177(3)
8.6 Summary
180(3)
IV Integration Of Design And Control
9 Design-For-Control-Based Integration
183(22)
9.1 Introduction
183(1)
9.2 Integration Problem
184(2)
9.3 Design-for-Control-Based Integration Methodology
186(6)
9.3.1 Design for Control
186(2)
9.3.2 Control Development
188(1)
9.3.3 Integration Optimization for Robust Pole Assignment
188(3)
9.3.4 Integration Procedure
191(1)
9.4 Case Study
192(12)
9.4.1 Design for Control
192(1)
9.4.2 Robust Pole Assignment
193(1)
9.4.3 Design Verification
193(9)
9.4.4 Design for Control
202(1)
9.4.5 Robust Dynamic Design and Verification
202(2)
9.5 Summary
204(1)
10 Intelligence-Based Hybrid Integration
205(24)
10.1 Introduction
205(2)
10.2 Problem in Hybrid System in Manufacturing
207(1)
10.3 Intelligence-Based Hybrid Integration
208(10)
10.3.1 Intelligent Process Control
208(6)
10.3.2 Hybrid Integration Design
214(1)
10.3.3 Hierarchical Optimization of Integration
215(3)
10.4 Case Study
218(9)
10.4.1 Objective
219(1)
10.4.2 Integration Method for the Curing Process
220(2)
10.4.3 Verification and Comparison
222(5)
10.5 Summary
227(2)
11 Conclusions
229(4)
11.1 Summary and Conclusions
229(2)
11.2 Challenge
231(2)
References 233(12)
Index 245
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, Peoples 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.
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