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Identification and Classical Control of Linear Multivariable Systems [Kõva köide]

(Indian Institute of Technology, Madras),
  • Formaat: Hardback, 425 pages, kõrgus x laius x paksus: 248x194x25 mm, kaal: 840 g, Worked examples or Exercises
  • Ilmumisaeg: 05-Jan-2023
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1316517217
  • ISBN-13: 9781316517215
Teised raamatud teemal:
Identification and Classical Control of Linear Multivariable SystemsSuurem pilt
  • Formaat: Hardback, 425 pages, kõrgus x laius x paksus: 248x194x25 mm, kaal: 840 g, Worked examples or Exercises
  • Ilmumisaeg: 05-Jan-2023
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1316517217
  • ISBN-13: 9781316517215
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781316517215.html
Märksõnad:
Most systems involved in a chemical process plant are interactive multivariable systems, to control which, the transfer function matrix model is required. This book considers the identification and control of such systems. It will be useful to students, researchers or practitioners who work with interactive, multivariable control systems.

Most systems involved in a chemical process plant are interactive multivariable systems, to control which, the transfer function matrix model is required. This lucid book considers the identification and control of such systems. It discusses open loop and closed loop identification methods, as well as the design of multivariable controllers based on steady state gain matrix. Simple methods for designing controllers based on transfer function matrix model have been reviewed. The design of controllers for non-square systems, and closed loop identification of multivariable unstable systems by the optimization method are also covered. Several simulation examples and exercise problems at the end of each chapter further help the reader consolidate the knowledge gained. This book will be useful to any engineering student, researcher or practitioner who works with interactive, multivariable control systems.

Muu info

This book trains engineering students to identify multivariable transfer function models and design classical controllers for such systems.
Preface xv
Acknowledgements xix
List of Abbreviations
xxiii
Notations xxv
1 Models, Control Theory, and Examples
1(20)
1.1 Classic Control Theory vs Modern Control Theory
1(1)
1.2 State Space Model Description
2(1)
1.3 Solution of Simultaneous Nonlinear Algebraic Equations
3(1)
1.4 State Space Representation of Ordinary Differential Equation (ODE) Model
4(2)
1.5 Transfer Function Derivation
6(1)
1.6 Numerical Solution of Simultaneous Nonlinear Algebraic Equations
7(2)
1.7 Conversion of Nth Order ODE to State Variable Form
9(1)
1.8 Coupled Higher Order Models
10(1)
1.9 Reported Transfer Function Matrix Models from Experiments
11(10)
1.9.1 Distillation Column for Binary Ethanol-Water Separation
11(1)
1.9.2 Wood and Berry Column
12(1)
1.9.3 Pilot Scale Acetone and Isopropyl Alcohol Distillation Column
13(1)
1.9.4 Babji and Saraf Distillation Column
13(1)
1.9.5 Crude Oil Distillation Process
14(1)
1.9.6 Two Coupled Pilot Plant Distillation Column
14(1)
1.9.7 A Reactor Control Problem
15(1)
1.9.8 Transfer Function Model of a Packed Distillation Column
16(1)
1.9.9 Transfer Function Model of a Distillation Column
16(1)
1.9.10 Transfer Function Model of an Industrial Distillation Column
17(4)
2 Identification and Control of SISO Systems
21(30)
2.1 Identification of SISO Systems
21(3)
2.1.1 Open Loop Identification of Stable Systems
22(1)
2.1.2 Identification of CSOPTD Systems
22(2)
2.2 Narasimha Reddy and Chidambaram (NO Method
24(5)
2.2.1 Identification of CSOPTD Model by NC method
24(2)
2.2.2 Identification of FOPTD Model
26(1)
2.2.3 Simulation Examples
27(1)
2.2.4 Comparison with SK Method
28(1)
2.3 Closed Loop Identification of Stable Systems
29(4)
2.4 Closed Loop Identification Method under PI Control
33(3)
2.4.1 Ananth and Chidambaram Method
33(3)
2.5 Optimization Method
36(4)
2.5.1 Simulation Examples
37(3)
2.6 Design of PI Controller
40(2)
2.6.1 FOPTD System by IMC Method
40(1)
2.6.2 SOPTD System with a Zero
41(1)
2.6.3 PID Settings by Skogestad Method
41(1)
2.6.4 PI and PID Settings by Stability Analysis Method
42(1)
2.7 Unstable Systems
42(2)
2.8 Estimation Methods
44(7)
2.8.1 Least Square Estimation of ARX Model
46(5)
3 Introduction to Linear Multivariable Systems
51(34)
3.1 Stable Square Systems
51(3)
3.2 Relative Gains
54(2)
3.3 Single Loop and Overall Stability
56(1)
3.4 Design of Decentralized SISO Controllers
57(3)
3.5 Biggest Log-Modulus Tuning
60(1)
3.6 Pairing Criteria for Unstable Systems
61(1)
3.7 Decoupling Control
62(3)
3.8 Gain and Phase Margin Method
65(1)
3.9 Relay Tuning Method
66(2)
3.10 Relay Tuning of MIMO Systems
68(1)
3.11 Improved Relay Analysis
69(2)
3.12 Simulation Examples
71(2)
3.13 Design of Simple Multivariable PI Controllers
73(1)
3.14 Design of Multivariable PID Controllers
74(2)
3.15 Performance and Robust Stability Analysis
76(9)
3.15.1 Input Multiplicative Uncertainty
79(1)
3.15.2 Output Multiplicative Uncertainty
79(1)
3.15.3 Using Maximum Singular Value
80(5)
4 CRC Method for Identifying TITO Systems
85(16)
4.1 Identification Method
86(3)
4.1.1 Identification of Individual Responses
86(2)
4.1.2 Identification of Transfer Function Model
88(1)
4.2 Simulation Examples
89(12)
5 CRC Method for Identifying SISO Systems by CSOPTD Models
101(16)
5.1 CSOPTD Systems
101(3)
5.1.1 Introduction
101(1)
5.1.2 Identification of Critically Damped SOPTD and FOPTD Systems
102(2)
5.2 Simulation Examples
104(10)
5.3 Simulation Study of a Nonlinear Bioreactor
114(3)
6 CRC Method for Identifying TITO Systems by CSOPTD Models
117(20)
6.1 Identification of Multivariable Systems
117(3)
6.2 Simulation Example
120(17)
6.2.1 Identification of Main Response
120(3)
6.2.2 Identification of Interaction Responses
123(3)
6.2.3 Effect of Measurement Noise
126(11)
7 Identification of Stable MIMO System by Optimization Method
137(20)
7.1 Identification of Decentralized Controlled Systems
137(5)
7.1.1 Identification Method
137(5)
7.2 Simulation Examples
142(15)
8 Identification of Centralized Controlled Multivariable Systems
157(20)
8.1 Identification Method
157(3)
8.2 Simulation Examples
160(11)
8.3 Another Method of Getting Guess Values
171(6)
9 Identification of Multivariable SOPTD Models by Optimization Method
177(28)
9.1 Introduction
177(2)
9.2 Identification Method
179(4)
9.3 Simulation Examples
183(11)
9.4 Method of Identifying CSOPTD Models
194(5)
9.5 Simulation Examples
199(6)
10 Identification of Unstable TITO Systems by Optimization Technique
205(18)
10.1 Identification of Systems with Decentralized PI Controllers
205(8)
10.1.1 Simulation Examples
208(5)
10.2 Identification of Systems Controlled by Centralized Controllers
213(10)
10.2.1 Identification of a Multivariable System
213(2)
10.2.2 Simulation Examples
215(8)
11 Centralized PI Controllers Based on Steady State Gain Matrix
223(18)
11.1 Introduction
223(1)
11.2 Multivariable System
224(1)
11.2.1 Davison Method
224(1)
11.3 Derivation of Controllers Design
225(2)
11.4 Simulation Studies
227(14)
11.4.1 Example 1: Wood and Berry Column
227(6)
11.4.2 Example 2: Industrial-Scale Polymerization (ISP) Reactor
233(2)
11.4.3 Example 3: Ogunnaike et al. Column
235(6)
12 SSGM Identification and Control of Unstable Multivariable Systems
241(24)
12.1 Introduction
241(1)
12.2 Identification of SSGM
242(1)
12.3 Multivariable Control System Design
243(1)
12.4 Design of Two-Stage Centralized PI Controllers
244(1)
12.5 Simulation Examples
245(2)
12.5.1 Example 1
245(2)
12.6 Design of Centralized PI Controllers for Example 1
247(2)
12.7 Design of Two-Stage Centralized PI Controllers for Example 1
249(6)
12.7.1 Design of Two-Stage P Controllers by Tanttu and Lieslehto Method
252(1)
12.7.2 PI Controllers for the Outer Loop by Tanttu and Lieslehto Method
253(2)
12.8 Robustness of the Control Systems
255(2)
12.8.1 Example 2
256(1)
12.9 Identification of SSGM for Example 2
257(8)
12.9.1 Single-Stage Multivariable Controllers for Example 2
257(2)
12.9.2 Two-Stage Centralized PI Controllers for Example 2
259(6)
13 Control of Stable Non-square MIMO Systems
265(36)
13.1 Introduction
266(2)
13.2 Davison Method, and Tanttu and Lieshleto Method for Stable Systems
268(3)
13.2.1 Davison Method
268(1)
13.2.2 Calculation of Tuning Parameters
268(1)
13.2.3 Tanttu and Lieslehto (TL) Method
269(1)
13.2.4 Robust Decentralized Controller
270(1)
13.3 Simulation Studies
271(2)
13.3.1 Example 1: Control of Distillation Column
271(1)
13.3.1.1 Davison Method for Distillation Columns
271(1)
13.3.1.2 Tanttu and Lieslehto Method for Distillation Columns
271(1)
13.3.1.3 Decentralized Controller Method for Distillation Columns
272(1)
13.4 Simulation Results
273(2)
13.4.1 Robustness Studies for Coupled Pilot Plant Distillation Column
274(1)
13.5 Simulation Example 2: Crude Distillation Process
275(8)
13.5.1 Davison Method for Crude Distillation Process
278(1)
13.5.2 Tanttu and Lieslehto Method
279(1)
13.5.3 Simulation Results
280(1)
13.5.4 Comparison of Controllers for Square and Non-square Crude Distillation Process
281(2)
13.6 Optimization Method for Controller Design for Non-square Stable Systems with RHP Zeros
283(9)
13.6.1 Genetic Algorithm
283(2)
13.6.2 Objective Function
285(1)
13.6.3 Controllers for Non-square Systems with RHP Zeros
286(1)
13.6.4 Design Example
286(1)
13.6.4.1 Centralized Controllers
287(1)
13.6.4.2 Decentralized Controller
287(2)
13.6.5 Comparison Criterion of Controller Performance
289(1)
13.6.6 Comparison of Controller Performance for Coupled Distillation Column
290(2)
13.6.7 Robustness Studies
292(1)
13.7 Auto-Tuning of PI Controllers for a Non-square System
292(5)
13.7.1 Design Procedure
294(1)
13.7.2 Simulation Study of a Non-square MIMO System
295(2)
13.8 Stability Criterion of Modified Inverse Nyquist Array on a Non-square Process
297(4)
14 Control of Unstable Non-square Systems
301(18)
14.1 Introduction
301(2)
14.2 Design of SSGM Based Multivariable Control
303(1)
14.2.1 Design of Centralized PI Controllers
303(1)
14.3 Simulation Studies
303(11)
14.3.1 Example 1
303(1)
14.3.1.1 Single-Stage Multivariable Controllers for Non-square Systems (Example 1)
304(1)
14.3.2 Two-Stage Centralized PI Controllers for Example 1
304(5)
14.3.3 Example 2 for Non-square systems
309(1)
14.3.3.1 Single-Stage Multivariable Controllers for Example 2
310(1)
14.3.3.2 Two-Stage Centralized PI Controllers for Example 2
310(4)
14.4 Identification of SSGM
314(5)
15 Trends in Control of Multivariable Systems
319(20)
15.1 Gain and Phase Margin Method
319(1)
15.2 Internal Model Control Method
320(2)
15.2.1 General Steps in IMC Design Procedure
321(1)
15.3 PI Controllers with No Proportional Kick
322(1)
15.4 Analytical Method
323(2)
15.5 Method of Inequalities
325(1)
15.6 Goal Attainment Method
326(1)
15.7 Effective Transfer Function Method
327(1)
15.8 Model Reference Controller
328(3)
15.9 Synthesis Method
331(2)
15.10 Model Reference Robust Control for MIMO Systems
333(1)
15.11 Variable Structure Control for Multivariable Systems
333(1)
15.12 Stabilizing Parametric Region of Multi Loop PID Controllers for Multivariable Systems
334(5)
Appendix A 339(12)
Appendix B 351(6)
Appendix C 357(14)
Bibliography 371(12)
Index 383
Dr V. Dhanya Ram is Assistant Professor, Department of Chemical Engineering at the National Institute of Technology, Calicut. She has 11 years of teaching and research experience. Dr M. Chidambaram is Professor Emeritus, Department of Chemical Engineering at the National Institute of Technology, Warangal.