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PID Control: New Identification and Design Methods 2005 ed. [Kõva köide]

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  • Formaat: Hardback, 544 pages, kõrgus x laius: 235x191 mm, kaal: 2860 g, XXVIII, 544 p., 1 Hardback
  • Ilmumisaeg: 17-Jun-2005
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1852337028
  • ISBN-13: 9781852337025
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PID Control: New Identification and Design Methods 2005 ed.Suurem pilt
  • Formaat: Hardback, 544 pages, kõrgus x laius: 235x191 mm, kaal: 2860 g, XXVIII, 544 p., 1 Hardback
  • Ilmumisaeg: 17-Jun-2005
  • Kirjastus: Springer London Ltd
  • ISBN-10: 1852337028
  • ISBN-13: 9781852337025
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781852337025.html
Märksõnad:
Demand for this book will be generated by the widespread use of PID in industry and because of the modern need for simple control systems to control a wider range of complex industrial processes and systems.

The effectiveness of proportional-integral-derivative (PID) controllers for a large class of process systems has ensured their continued and widespread use in industry. Similarly there has been a continued interest from academia in devising new ways of approaching the PID tuning problem.To the industrial engineer and many control academics this work has previously appeared fragmented but a key determinant of this literature is the type of process model information used in the PID tuning methods. PID Control presents a set of coordinated contributions illustrating methods, old and new, that cover the range of process model assumptions systematically. After a review of PID technology, these contributions begin with model-free methods, progress through non-parametric model methods (relay experiment and phase-locked-loop procedures), visit fuzzy-logic- and genetic-algorithm-based methods; introduce a novel subspace identification method before closing with an interesting set of parametric model techniques including a chapter on predictive PID controllers.Highlights of PID Control include: an introduction to PID control technology features and typical industrial implementations; chapter contributions ordered by the increasing quality of the model information used;novel PID control concepts for multivariable processes. PID Control will be useful to industry-based engineers wanting a better understanding of what is involved in the steps to a new generation of PID controller techniques. Academics wishing to have a broader perspective of PID control research and development will find useful pedagogical material and research ideas in this text.
Editorial Responsibilities xix
Notation xxv
1 PID Control Technology 1(46)
Learning Objectives
1(1)
1.1 Basic Industrial Control
2(5)
1.1.1 Process Loop Issues - a Summary Checklist
6(1)
1.2 Three-Term Control
7(10)
1.2.1 Parallel PID Controllers
9(1)
1.2.2 Conversion to Time constant PID Forms
10(2)
1.2.3 Series PID Controllers
12(2)
1.2.4 Simple PID Tuning
14(3)
1.3 PID Controller Implementation Issues
17(12)
1.3.1 Bandwidth-Limited Derivative Control
18(4)
1.3.2 Proportional Kick
22(2)
1.3.3 Derivative Kick
24(2)
1.3.4 Integral Anti-Windup Circuits
26(3)
1.3.5 Reverse-Acting Controllers
29(1)
1.4 Industrial PID Control
29(17)
1.4.1 Traditional Industrial PID Terms
30(2)
1.4.2 Industrial PID Structures and Nomenclature
32(1)
1.4.3 The Process Controller Unit
33(2)
1.4.4 Supervisory Control and the SCADA PID Controller
35(11)
Acknowledgements
46(1)
References
46(1)
2 Some PID Control Fundamentals 47(62)
Learning Objectives
47(1)
2.1 Process System Models
48(9)
2.1.1 State Space Models
49(3)
2.1.2 Convolution Integral Process Models
52(1)
2.1.3 Laplace Transfer Function Models
53(2)
2.1.4 Common Laplace Transform Process Models
55(2)
2.2 Controller Degrees of Freedom Structure
57(3)
2.2.1 One Degree of Freedom Control
57(1)
2.2.2 Two Degree of Freedom Control
57(2)
2.2.3 Three Degree of Freedom Structures
59(1)
2.3 PID Control Performance
60(28)
2.3.1 Controller Performance Assessment - General Considerations
60(6)
2.3.2 Controller Assessment - the Effectiveness of PID Control
66(7)
2.3.3 Classical Stability Robustness Measures
73(6)
2.3.4 Parametric Stability Margins for Simple Processes
79(9)
2.4 State Space Systems and PID Control
88(11)
2.4.1 Linear Reference Error Feedback Control
88(2)
2.4.2 Two Degree of Freedom Feedback Control System
90(1)
2.4.3 State Feedback With Integral Error Feedback Action
91(4)
2.4.4 State Space Analysis for Classical PI Control Structure
95(4)
2.5 Multivariable PID Control Systems
99(7)
2.5.1 Multivariable Control
100(3)
2.5.2 Cascade Control Systems
103(3)
Acknowledgements
106(1)
References
106(3)
3 On-line Model-Free Methods 109(38)
Learning Objectives
109(1)
3.1 Introduction
110(4)
3.1.1 A Model-Free Control Design Paradigm
110(4)
3.2 Iterative Feedback Tuning
114(10)
3.2.1 Generating the Cost Function Gradient
114(3)
3.2.2 Case Study - a Wastewater Process Example
117(5)
3.2.3 Some Remarks on Iterative Feedback Tuning
122(2)
3.3 The Controller Parameter Cycling Tuning Method
124(19)
3.3.1 Generating the Gradient and Hessian - Some Theory
125(6)
3.3.2 Issues for a Controller Parameter Cycling Algorithm
131(4)
3.3.3 The Controller Parameter Cycling Algorithm
135(1)
3.3.4 Case Study - Multivariable Decentralised Control
136(7)
3.4 Summary and Future Directions
143(1)
Acknowledgements
144(1)
Appendix 3.A
144(1)
References
145(2)
4 Automatic PID Controller Tuning - the Nonparametric Approach 147(36)
Learning Objectives
147(1)
4.1 Introduction
148(1)
4.2 Overview of Nonparametric Identification Methods
149(3)
4.2.1 Transient Response Methods
149(1)
4.2.2 Relay Feedback Methods
150(1)
4.2.3 Fourier Methods
150(1)
4.2.4 Phase-Locked Loop Methods
151(1)
4.3 Frequency Response Identification with Relay Feedback
152(14)
4.3.1 Basic Idea
153(2)
4.3.2 Improved Estimation Accuracy
155(6)
4.3.3 Estimation of a General Point
161(3)
4.3.4 Estimation of Multiple Points
164(1)
4.3.5 On-line relay tuning
164(2)
4.4 Sensitivity Assessment Using Relay Feedback
166(5)
4.4.1 Control Robustness
166(1)
4.4.2 Maximum Sensitivity
167(1)
4.4.3 Construction of the 2 -0 Chart
168(2)
4.4.4 Stability Margins Assessment
170(1)
4.5 Conversion to Parametric Models
171(3)
4.5.1 Single and Multiple Lag Processes
172(2)
4.5.2 Second-Order Modelling
174(1)
4.6 Case Studies
174(6)
Case Study 4.1: Improved Estimation Accuracy for the Relay Experiment
176(1)
Case Study 4.2: Estimation of a General Point
177(1)
Case Study 4.3: Estimation of Multiple Points
177(2)
Case Study 4.4: On-line Relay Tuning
179(1)
Case Study 4.5: Sensitivity Assessment
179(1)
References
180(3)
5 Relay Experiments for Multivariable Systems 183(30)
Learning Objectives
183(1)
5.1 Introduction
184(1)
5.2 Critical Points of a System
185(2)
5.2.1 Critical Points for Two-Input, Two-Output Systems
185(1)
5.2.2 Critical Points for MIMO Systems
186(1)
5.3 Decentralised Relay Experiments for Multivariable Systems
187(10)
5.3.1 Finding System Gains at Particular Frequencies
188(2)
5.3.2 Decentralised Relay Control Systems - Some Theory
190(1)
5.3.3 A Decentralised Two-Input, Two-Output PID Control System Relay-Based Procedure
191(6)
5.4 A Decentralised Multi-Input, Multi-Output PID Control System Relay-Based Procedure
197(5)
5.5 PID Control Design at Bandwidth Frequency
202(5)
5.6 Case Studies
207(3)
5.6.1 Case Study 1: The Wood and Berry Process System Model
207(3)
5.6.2 Case Study 2: A Three-Input, Three-Output Process System
210(1)
5.7 Summary
210(1)
References
211(2)
6 Phase-Locked Loop Methods 213(46)
Learning Objectives
213(1)
6.1 Introduction
214(7)
6.1.1 The Relay Experiment
215(1)
6.1.2 Implementation Issues for the Relay Experiment
216(4)
6.1.3 Summary Conclusions on the Relay Experiment
220(1)
6.2 Some Constructive Numerical Solution Methods
221(8)
6.2.1 Bisection Method
222(2)
6.2.2 Prediction Method
224(3)
6.2.3 Bisection and Prediction Method - a Comparison and Assessment
227(2)
6.3 Phase-Locked Loop Identifier Module - Basic Theory
229(26)
6.3.1 The Digital Identifier Structure
230(12)
6.3.2 Noise Management Techniques
242(6)
6.3.3 Disturbance Management Techniques
248(7)
6.4 Summary and Discussion
255(1)
References
256(3)
7 Phase-Locked Loop Methods and PID Control 259(38)
Learning Objectives
259(1)
7.1 Introduction - Flexibility and Applications
260(1)
7.2 Estimation of the Phase Margin
260(1)
7.3 Estimation of the Parameters of a Second-Order Underdamped System
261(4)
7.4 Identification of Systems in Closed Loop
265(5)
7.4.1 Identification of an Unknown System in Closed Loop with an Unknown Controller
265(3)
7.4.2 Identification of an Unknown System in Closed Loop with a Known Controller
268(2)
7.5 Automated PI Control Design
270(24)
7.5.1 Identification Aspects for Automated PID Control Design
271(4)
7.5.2 PI Control with Automated Gain and Phase Margin Design
275(11)
7.5.3 PI Control with Automated Maximum Sensitivity and Phase Margin Design
286(8)
7.6 Conclusions
294(1)
References
295(2)
8 Process Reaction Curve and Relay Methods Identification and PID Tuning 297(42)
Learning Objectives
297(1)
8.1 Introduction
298(4)
8.2 Developing Simple Models from the Process Reaction Curve
302(8)
8.2.1 Identification Algorithm for Oscillatory Step Responses
303(2)
8.2.2 Identification Algorithm for Non-Oscillatory Responses Without Overshoot
305(5)
8.3 Developing Simple Models from a Relay Feedback Experiment
310(10)
8.3.1 On-line Identification of FOPDT Models
312(2)
8.3.2 On-line Identification of SOPDT Models
314(1)
8.3.3 Examples for the On-line Relay Feedback Procedure
315(2)
8.3.4 Off-line Identification
317(3)
8.4 An Inverse Process Model-Based Design Procedure for PID Control
320(9)
8.4.1 Inverse Process Model-Based Controller Principles
320(3)
8.4.2 PI/PID Controller Synthesis
323(2)
8.4.3 Autotuning of PID Controllers
325(4)
8.5 Assessment of PI/PID Control Performance
329(7)
8.5.1 Achievable Minimal IAE Cost and Rise Time
329(3)
8.5.2 Assessment of PI/PID Controllers
332(4)
References
336(3)
9 Fuzzy Logic and Genetic Algorithm Methods in PID Tuning 339(22)
Learning Objectives
339(1)
9.1 Introduction
340(1)
9.2 Fuzzy PID Controller Design
340(10)
9.2.1 Fuzzy PI Controller Design
342(1)
9.2.2 Fuzzy D Controller Design
343(1)
9.2.3 Fuzzy PID Controller Design
344(1)
9.2.4 Fuzzification
345(1)
9.2.5 Fuzzy Control Rules
346(1)
9.2.6 Defuzzification
346(3)
9.2.7 A Control Example
349(1)
9.3 Multi-Objective Optimised Genetic Algorithm Fuzzy PID Control
350(5)
9.3.1 Genetic Algorithm Methods Explained
351(2)
9.3.2 Case study A: Multi-Objective Genetic Algorithm Fuzzy PID Control of a Nonlinear Plant
353(1)
9.3.3 Case study B: Control of Solar Plant
354(1)
9.4 Applications of Fuzzy PID Controllers to Robotics
355(2)
9.5 Conclusions and Discussion
357(1)
Acknowledgments
358(1)
References
358(3)
10 Tuning PID Controllers Using Subspace Identification Methods 361(28)
Learning Objectives
361(1)
10.1 Introduction
362(1)
10.2 A Subspace Identification Framework for Process Models
363(5)
10.2.1 The Subspace Identification Framework
363(3)
10.2.2 Incremental Subspace Representations
366(2)
10.3 Restricted Structure Single-Input, Single-Output Controllers
368(3)
10.3.1 Controller Parameterisation
369(1)
10.3.2 Controller Structure and Computations
370(1)
10.4 Restricted-Structure Multivariable Controller Characterisation
371(1)
10.4.1 Controller Parameterisation
371(1)
10.4.2 Multivariable Controller Structure
372(1)
10.5 Restricted-Structure Controller Parameter Computation
372(4)
10.5.1 Cost Index
373(1)
10.5.2 Formulation as a Least-Squares Problem
373(1)
10.5.3 Computing the Closed-Loop System Condition
374(1)
10.5.4 Closed-Loop Stability Conditions
375(1)
10.5.5 The Controller Tuning Algorithm
375(1)
10.6 Simulation Case Studies
376(11)
10.6.1 Activated Sludge Wastewater Treatment Plant Layout
377(1)
10.6.2 Case study 1: Single-Input, Single-Output Control Structure
378(1)
10.6.3 Case Study 2: Control of Two Reactors with a Lower Triangular Controller Structure
379(3)
10.6.4 Case Study 3: Control of Three Reactors with a Diagonal Controller Structure
382(3)
10.6.5 Case Study 4: Control of Three Reactors with a Lower Triangular Controller Structure
385(2)
References
387(2)
11 Design of Multi-Loop and Multivariable PID Controllers 389(40)
Learning Objectives
389(1)
11.1 Introduction
390(4)
11.1.1 Multivariable Systems
390(1)
11.1.2 Multivariable Control
391(1)
11.1.3 Scope of the
Chapter and Some Preliminary Concepts
392(2)
11.2 Multi-Loop PID Control
394(14)
11.2.1 Biggest Log-Modulus Tuning Method
394(1)
11.2.2 Dominant Pole Placement Tuning Method
395(9)
11.2.3 Examples
404(4)
11.3 Multivariable PID Control
408(18)
11.3.1 Decoupling Control and Design Overview
409(3)
11.3.2 Determination of the Objective Loop Performance
412(9)
11.3.3 Computation of PID Controller
421(1)
11.3.4 Examples
422(4)
11.4 Conclusions
426(1)
References
427(2)
12 Restricted Structure Optimal Control 429(44)
Learning Objectives
429(1)
12.1 Introduction to Optimal LQG Control for Scalar Systems
430(6)
12.1.1 System Description
431(1)
12.1.2 Cost Function and Optimisation Problem
432(4)
12.2 Numerical Algorithms for SISO System Restricted Structure Control
436(5)
12.2.1 Formulating a Restricted Structure Numerical Algorithm
436(3)
12.2.2 Iterative Solution for the SISO Restricted Structure LQG Controller
439(1)
12.2.3 Properties of the Restricted Structure LQG Controller
440(1)
12.3 Design of PID Controllers Using the Restricted Structure Method
441(3)
12.3.1 General Principles for Optimal Restricted Controller Design
442(1)
12.3.2 Example of PID Control Design
443(1)
12.4 Multivariable Optimal LQG Control: An Introduction
444(9)
12.4.1 Multivariable Optimal LQG Control and Cost Function Values
448(3)
12.4.2 Design Procedures for an Optimal LQG Controller
451(2)
12.5 Multivariable Restricted Structure Controller Procedure
453(8)
12.5.1 Analysis for a Multivariable Restricted Structures Algorithm
454(4)
12.5.2 Multivariable Restricted Structure Algorithm and Nested Restricted Structure Controllers
458(3)
12.6 An Application of Multivariable Restricted Structure Assessment - Control of the Hotstrip Finishing Mill Looper System
461(9)
12.6.1 The Hotstrip Finishing Mill Looper System
461(2)
12.6.2 An Optimal Multivariable LQG Controller for the Looper System
463(2)
12.6.3 A Controller Assessment Exercise for the Hotstrip Looper System
465(5)
12.7 Conclusions
470(1)
Acknowledgements
471(1)
References
472(1)
13 Predictive PID Control 473(58)
Learning Objectives
473(1)
13.1 Introduction
474(1)
13.2 Classical Process Control Model Methods
475(10)
13.2.1 Smith Predictor Principle
475(2)
13.2.2 Predictive PI With a Simple Model
477(3)
13.2.3 Method Application and an Example
480(5)
13.3 Simple Process Models and GPC-Based Methods
485(15)
13.3.1 Motivation for the Process Model Restriction
485(1)
13.3.2 Analysis for a GPC PID Controller
486(4)
13.3.3 Predictive PID Control: Delay-Free System h = 0
490(1)
13.3.4 Predictive PID Control: Systems with Delay h > 0
491(2)
13.3.5 Predictive PID Control: An Illustrative Example
493(7)
13.4 Control Signal Matching and GPC Methods
500(24)
13.4.1 Design of SISO Predictive PID Controllers
500(3)
13.4.2 Optimal Values of Predictive PID Controller Gains
503(12)
13.4.3 Design of MIMO Predictive PID controllers
515(9)
Acknowledgements
524(1)
Appendix 13.A
524(5)
13.A.1 Proof of Lemma 13.1
524(2)
13.A.2 Proof of Theorem 13.1
526(1)
13.A.3 Proof of Lemma 13.2
527(2)
References
529(2)
About the Contributors 531(8)
Index 539