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E-raamat: PID Tuning: A Modern Approach via the Weighted Sensitivity Problem [Taylor & Francis e-raamat]

(UAB, Bellaterra, BCN, SPAIN), (UAB, Bellaterra, BCN, SPAIN), (UAB, Bellaterra, BCN, SPAIN)
  • Formaat: 154 pages, 20 Tables, black and white; 74 Illustrations, black and white
  • Ilmumisaeg: 20-Nov-2020
  • Kirjastus: CRC Press
  • ISBN-13: 9780429325335
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  • Taylor & Francis e-raamat
  • Hind: 170,80 €*
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  • Tavahind: 244,00 €
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PID Tuning: A Modern Approach via the Weighted Sensitivity ProblemSuurem pilt
  • Formaat: 154 pages, 20 Tables, black and white; 74 Illustrations, black and white
  • Ilmumisaeg: 20-Nov-2020
  • Kirjastus: CRC Press
  • ISBN-13: 9780429325335
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429325335
The PID controller is the most common option in the realm of control applications and is dominant in the process control industry. Among the related analytical methods, Internal Model Control (IMC) has gained remarkable industrial acceptance due to its robust nature and good set-point responses. However, the traditional application of IMC results in poor load disturbance rejection for lag-dominant and integrating plants. This book presents an IMC-like design method which avoids this common pitfall and is devised to work well for plants of modest complexity, for which analytical PID tuning is plausible. For simplicity, the design only focuses on the closed-loop sensitivity function, including formulations for the H and H2 norms. Aimed at graduate students and researchers in control engineering, this book:











Considers both the robustness/performance and the servo/regulation trade-offs





Presents a systematic, optimization-based approach, ultimately leading to well-motivated, model-based, and analytically derived tuning rules





Shows how to tune PID controllers in a unified way, encompassing stable, integrating, and unstable processes





Finds in the Weighted Sensitivity Problem the sweet spot of robust, optimal, and PID control





Provides a common analytical framework that generalizes existing tuning proposals
Foreword xi
Preface xiii
Authors xv
1 Introduction
1(16)
1.1 Servo, regulation, and stability
1(1)
1.2 Industrial PID control
2(1)
1.3 Internal model and H∞ control
3(5)
1.3.1 Internal model control
3(1)
1.3.2 H∞ control
4(1)
1.3.3 Blending internal model and H∞ control
5(1)
1.3.4 Vilanova's (2008) design for robust PID tuning revisited
6(2)
1.4 Outline of the book
8(9)
I MODEL-MATCHING APPROACH TO ROBUST PID DESIGN
17(54)
2 Simple Model-Matching Approach to Robust PID Control
19(20)
2.1 Problem statement
19(3)
2.1.1 The control framework
19(1)
2.1.2 The model-matching problem
20(1)
2.1.3 The model-matching problem within Hoc control
21(1)
2.2 Analytical solution
22(4)
2.2.1 Initial formulation for set-point response
22(2)
2.2.2 Alternative formulation
24(2)
2.3 Stability analysis
26(3)
2.3.1 Nominal stability
26(2)
2.3.2 Robust stability
28(1)
2.4 Automatic PID tuning derivation
29(4)
2.4.1 Control effort constraints
32(1)
2.5 Simulation examples
33(6)
3 Alternative Design for Load Disturbance Improvement
39(18)
3.1 Problem statement
39(2)
3.1.1 The control framework
40(1)
3.1.2 The model-matching problem formulation
41(1)
3.2 Model-matching solution for PID design
41(2)
3.3 Trade-off tuning interval considering load disturbances
43(4)
3.3.1 Nominal stability
45(2)
3.4 Tuning guidelines
47(2)
3.5 Simulation examples
49(8)
4 Analysis of the Smooth/Tight-Servo/Regulation Tuning Approaches
57(14)
4.1 Revisiting the model-matching designs
57(1)
4.2 Smooth/tight tuning
58(1)
4.3 Servo/regulation tuning
59(3)
4.4 Implementation aspects
62(1)
4.5 Simulation examples
63(5)
4.6 Summary
68(3)
II WEIGHT SELECTION FOR SENSITIVITY SHAPING
71(34)
5 H∞ Design with Application to PI Tuning
73(16)
5.1 Problem statement
73(1)
5.2 Analytical solution
74(3)
5.3 Weight selection
77(1)
5.4 Stability and robustness analysis
78(2)
5.5 Application to PI tuning
80(2)
5.5.1 Stable/unstable plants
80(1)
5.5.2 Integrating plant case (τ → ∞)
81(1)
5.6 Simulation examples
82(7)
6 Generalized IMC Design and H2 Approach
89(16)
6.1 Motivation for the input/output disturbance trade-off
89(2)
6.2 Problem statement
91(1)
6.3 Weight selection
92(1)
6.4 Analytical solution
93(4)
6.4.1 Interpretation in terms of alternative IMC filters
95(1)
6.4.2 Extension to plants with integrators or complex poles
96(1)
6.5 Performance and robustness analysis
97(1)
6.6 Tuning guidelines
98(1)
6.7 Simulation examples
98(7)
III WEIGHTED SENSITIVITY APPROACH FOR ROBUST PID TUNING
105(22)
7 PID Design as a Weighted Sensitivity Problem
107(8)
7.1 Context, motivation, and objective
107(1)
7.2 Servo/regulation and robustness/performance trade-offs
108(1)
7.3 Unifying tuning rules
109(2)
7.4 Special cases and tuning-rule simplifications
111(2)
7.4.1 First-order cases (τ2 = 0)
111(1)
7.4.2 Second-order cases
112(1)
7.5 Applicability: normalized dead time range
113(2)
8 PID Tuning Guidelines for Balanced Operation
115(12)
8.1 Robustness and comparable servo/regulation designs
115(1)
8.2 Servo/regulation performance evaluation: Jmax and Javg indices
116(2)
8.3 PI control using first-order models
118(5)
8.3.1 Stable and integrating cases
118(1)
8.3.1.1 Tuning based on Jmax
119(1)
8.3.1.2 Tuning based on Javg
120(1)
8.3.2 Unstable case
120(1)
8.3.2.1 Tuning based on Jmax and Javg
120(3)
8.4 PID control using second-order models
123(4)
8.4.1 Stable and integrating cases
123(1)
8.4.1.1 Tuning based on Jmax
123(1)
8.4.1.2 Tuning based on Javg
124(1)
8.4.2 Unstable case
125(1)
8.4.2.1 Tuning based on Jmax and Javg
125(2)
Appendix A 127(4)
Bibliography 131(6)
Index 137
Salvador Alcántara Cano graduated in Computer Science & Engineering and then obtained the MSc and PhD degrees in Systems Engineering & Automation, all from Universitat Autònoma de Barcelona, in 2005, 2008, and 2011, respectively. During his short-lived research career, he focused on PID control and the analytical derivation of simple tuning rules guided by robust and optimal principles. He also made two research appointments with Professors Weidong Zhang and Sigurd Skogestad, almost completed a degree in Mathematics, and held a Marie Curie postdoctoral position in the Netherlands. Back in Barcelona, "Salva" worked as an automation & control practitioner for one more year, before definitively shifting his career into software development. Apart from programming and DevOps in general, his current interests include Stream Processing, Machine Learning, and Functional Programming & Category Theory.

Ramon Vilanova Arbós graduated from the Universitat Autònoma de Barcelona (1991), obtaining the title of Doctor through the same university (1996). At present, he's Full Professor of Automatic Control and Systems Engineering at the School of Engineering of the Universitat Autònoma de Barcelona where he develops educational task-teaching subjects of Signals and Systems, Automatic Control, and Technology of Automated Systems. His research interests include methods of tuning of PID regulators, systems with uncertainty, analysis of control systems with several degrees of freedom, applications to environmental systems, and development of methodologies for the design of machine-man interfaces. He is an author of several book chapters and has more than 100 publications in international congresses/journals. He is a member of IFAC and IEEE-IES. He's also a member of the Technical Committee on Factory Automation.

Carles Pedret i Ferré was born in Tarragona, Spain, on January 29, 1972. He received the BSc degree in Electronic Engineering and the PhD degree in System Engineering and Automation from Universitat Autònoma de Barcelona, in 1997 and 2003, respectively. He is Associate Professor at the Department of Telecommunications and System Engineering of Universitat Autònoma de Barcelona. His research interests are in uncertain systems, time-delay systems, and PID control.
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