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Data-driven Controller Design

The H2 Approach

Data-driven Controller Design - Eckhard, Diego; Campestrini, Lucíola; Sanfelice Bazanella, Alexandre - ISBN: 9789400722996
Prijs: € 114,81
Levertijd: 3 tot 5 werkdagen
Bindwijze: Boek, Gebonden
Genre: Bedrijfsinformatietechnologie
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Beschrijving

This Book Presents A Comprehensive Theoretical Treatment Of The H2 Approach To Data-driven Control Design. It Features A Large Number Of Practical Designs Performed For Different Classes Of Processes: Thermal, Fluid Processing And Electromechanical.

Details

Titel: Data-driven Controller Design
Auteur: Eckhard, Diego; Campestrini, Lucíola; Sanfelice Bazanella, Alexandre
Mediatype: Boek
Bindwijze: Gebonden
Taal: Engels
Aantal pagina's: 228
Uitgever: Springer
NUR: Bedrijfsinformatietechnologie
Afmetingen: 234 x 156 x 14
Gewicht: 498 gr
ISBN/ISBN13: 9789400722996
Intern nummer: 18180216

Inhoudsopgave

1 Definitions
1(6)
1.1 The Process
1(2)
1.2 The Control
3(2)
1.3 The Closed-Loop System
5(1)
1.4 The Design Problem
6(1)
References
6(1)
2 H2 Performance Criteria
7(20)
2.1 Introduction
7(1)
2.2 The Different Criteria
8(4)
2.2.1 Reference Tracking---The Model Reference Control
8(1)
2.2.2 Noise Rejection---The Minimum Variance Control
9(1)
2.2.3 The Composite Performance
10(1)
2.2.4 Economy of Control Effort
11(1)
2.3 Duality with System Identification---The Ideal Controllers
12(7)
2.3.1 Reference Tracking
12(4)
2.3.2 Noise Rejection
16(2)
2.3.3 The Composite Criterion
18(1)
2.4 Beware of What You Ask for---Choosing the Reference Model
19(5)
2.4.1 Too Ambitious Performance
21(3)
2.5 Chapter Conclusions
24(3)
References
25(2)
3 One-Shot Optimization---The VRFT Method
27(42)
3.1 Introduction
27(1)
3.2 The Ideal Case
27(7)
3.2.1 Generation of the Virtual Reference---A Caveat
32(2)
3.3 The Mismatched Case
34(5)
3.4 Dealing with Non-minimum Phase Plants
39(6)
3.4.1 The Flexible VRFT Criterion
41(2)
3.4.2 Implementation Issues
43(1)
3.4.3 Two-Step Procedure
44(1)
3.5 The Noisy Case
45(4)
3.5.1 Choosing the Instrumental Variable
47(2)
3.6 Case Studies
49(16)
3.6.1 The Need of Instrumental Variables
50(4)
3.6.2 Applying the Flexible Criterion
54(11)
3.7 Chapter Conclusions
65(4)
References
66(3)
4 Iterative Optimization
69(20)
4.1 Some Things to Remember from Calculus
69(3)
4.2 Optimization Algorithms (Dynamic Systems)
72(3)
4.2.1 Autonomous Systems
75(1)
4.3 The Basic Algorithms and Their Convergence
75(12)
4.3.1 Steepest Descent
75(5)
4.3.2 Other Search Directions
80(1)
4.3.3 Newton-Raphson
81(4)
4.3.4 Robustness
85(2)
4.4 Chapter Conclusions
87(2)
References
88(1)
5 Convergence to the Globally Optimal Controller
89(30)
5.1 Jy---The Reference Tracker
90(12)
5.2 Je---The Variance Minimizer
102(2)
5.3 The Mismatched Case
104(4)
5.4 Choosing the Algorithm Parameters
108(4)
5.4.1 The Search Direction
108(1)
5.4.2 The Step Sizes---First Solution
109(1)
5.4.3 The Step Sizes---Second Solution
110(2)
5.5 A Case Study
112(3)
5.6 Chapter Conclusions
115(4)
References
117(2)
6 Cost Function Shaping
119(26)
6.1 Introduction
119(1)
6.2 The Problem Data
119(2)
6.3 Cautious Control
121(3)
6.4 Manipulation of the Reference Spectrum
124(9)
6.4.1 Properties of the Sensitivity
125(2)
6.4.2 Applying a Different Reference
127(2)
6.4.3 Windowing
129(4)
6.5 Case Studies
133(9)
6.5.1 A PID Design
133(5)
6.5.2 A Noisy Process
138(4)
6.6 Chapter Conclusions
142(3)
References
144(1)
7 Computations
145(20)
7.1 Iterative Feedback Tuning
146(8)
7.1.1 Derivation
146(4)
7.1.2 Extensions
150(4)
7.2 Frequency Domain Tuning
154(5)
7.2.1 Derivation
155(3)
7.2.2 Extensions
158(1)
7.3 Correlation-Based Tuning
159(4)
7.3.1 Derivation
160(3)
7.4 Chapter Conclusions
163(2)
References
163(2)
8 Experimental Results
165
8.1 A Liquid Flow Process
166(11)
8.1.1 Direct Method
169(5)
8.1.2 Iterative Method
174(3)
8.2 A DC Motor
177(10)
8.2.1 Direct Method
178(8)
8.2.2 Iterative Method
186(1)
8.3 A Temperature Process
187
8.3.1 The Favorable Scenario
187(7)
8.3.2 Tough Case
194(13)
References
207

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