Atjaunināt sīkdatņu piekrišanu

Modeling, Analysis And Design Of Control Systems In Matlab And Simulink [Hardback]

5.00/5 (2 ratings by Goodreads)
(Northeastern Univ, China), (Univ Of California, Merced, Usa)
  • Formāts: Hardback, 580 pages
  • Izdošanas datums: 14-Nov-2014
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9814618454
  • ISBN-13: 9789814618458
Citas grāmatas par šo tēmu:
  • Hardback
  • Cena: 243,28 €
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
Modeling, Analysis And Design Of Control Systems In Matlab And SimulinkPalielināt
  • Formāts: Hardback, 580 pages
  • Izdošanas datums: 14-Nov-2014
  • Izdevniecība: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9814618454
  • ISBN-13: 9789814618458
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9789814618458.html
Keywords:
MATLAB and Simulink are now being used extensively in not only academia as a teaching aid, a learning aid and a research tool but also industry for modeling, analysis, design and rapid prototyping. As a response, Modeling, Analysis and Design of Control Systems in MATLAB and Simulink emphasizes on practical use of and problem solving in MATLAB and Simulink following the so-called MAD (modeling, analysis and design) notion. Readers can not only learn the control concepts and problem solving methods but also coding skills by following the numerous inline MATLAB scripts, functions, reproducible examples as well as chapter-end Problems. The book service website http://mechatronics.ucmerced.edu/MADbook contains Solution Manual, 1,000 plus teaching/learning PPTs, and all related codes used in the book for reproducing the examples.Modeling, Analysis and Design of Control Systems in MATLAB and Simulink has 12 chapters organized in 5 parts: Foundation, Modeling, Analysis, Design and Rapid Prototyping. Each chapter ends with Problems section. This book can be used as a reference text in the introductory control course for undergraduates in all engineering schools. The coverage of topics is broad, yet balanced, and it should provide a solid foundation for the subsequent control engineering practice in both industry and research institutes. This book will be a good desktop reference for control engineers and many codes and tools in this book may be directly applicable in real world problem solving.
Foreword v
Preface ix
1 Introduction to Simulation and Computer-aided Design of Control Systems 1(16)
1.1 A Brief Historic Review of the Development of Computer-aided Design of Control Systems
1(1)
1.2 Introduction to the CACSD Languages and Environments
2(3)
1.3 Development of Simulation Software
5(1)
1.4 MATLAB/Simulink and Their CACSD Toolboxes
6(2)
1.5 Overview of CACSD Approaches
8(2)
1.6 Fundamental Structures and Contents of This Book
10(2)
1.7 Problems
12(2)
Bibliography and References
14(3)
2 Fundamentals of MATLAB Programming 17(44)
2.1 Basics in MATLAB Programming
18(4)
2.1.1 Variables and Constants in MATLAB
18(1)
2.1.2 Data Structures
19(1)
2.1.3 Basic Statement Structures of MATLAB
20(1)
2.1.4 Colon Expressions
21(1)
2.1.5 Sub-matrix Extraction
22(1)
2.2 Basic Mathematical Operations
22(5)
2.2.1 Algebraic Calculations of Matrices
22(2)
2.2.2 Logic Operations of Matrices
24(1)
2.2.3 Relationship Operations of Matrices
24(1)
2.2.4 Simplifications and Presentations of Analytical Results
24(2)
2.2.5 Basic Number Theory Computations
26(1)
2.3 Flow Control Structures in MATLAB Programming
27(3)
2.3.1 Loop Control Structures
27(1)
2.3.2 Conditional Structure
28(1)
2.3.3 Switch Structure
29(1)
2.3.4 Trial Structure
29(1)
2.4 Function Writing and Debugging
30(4)
2.4.1 Basic Structure of MATLAB Functions
30(3)
2.4.2 Functions with Variable Numbers of Inputs and Outputs
33(1)
2.4.3 Anonymous and Inline Functions
33(1)
2.4.4 Pseudo Codes
34(1)
2.5 Two-dimensional Graphics
34(7)
2.5.1 Basic Statements of Two-dimensional Plotting
34(3)
2.5.2 Other Graphics Functions with Applications
37(2)
2.5.3 Implicit Function Visualizations
39(1)
2.5.4 Graph Editing and Decorations
39(2)
2.6 Three-dimensional Visualization
41(4)
2.6.1 Three-dimensional Curves
41(1)
2.6.2 Three-dimensional Surfaces
41(3)
2.6.3 Viewpoint Setting in 3D Plots
44(1)
2.7 Graphical User Interface Design in MATLAB
45(12)
2.7.1 Graphical User Interface Tool - Guide
46(1)
2.7.2 Handle Graphics and Properties of Objects
46(6)
2.7.3 Menu System Design
52(1)
2.7.4 An Illustrative Example in GUI Design
52(2)
2.7.5 Toolbar Design
54(2)
2.7.6 Embedding ActiveX Components in GUIs
56(1)
2.8 Problems
57(3)
Bibliography and References
60(1)
3 MATLAB Solutions to Scientific Computation Problems 61(34)
3.1 MATLAB Solutions to Linear Algebra Problems
62(4)
3.1.1 Fundamental Analysis of Matrices
62(2)
3.1.2 Matrix Decomposition
64(2)
3.1.3 Matrix Exponential eA and Exponential Function eAt
66(1)
3.2 Solutions of Algebraic Equations
66(8)
3.2.1 Solutions of Linear Algebraic Equations
66(3)
3.2.2 Solutions of Nonlinear Equations
69(3)
3.2.3 Solutions of Nonlinear Matrix Equations
72(2)
3.3 Solutions of Ordinary Differential Equations
74(7)
3.3.1 Numerical Solutions to First-order Explicit ODEs
74(3)
3.3.2 Conversions of ODEs
77(1)
3.3.3 Validations of Numerical Solutions
78(2)
3.3.4 Analytical Solutions to Linear ODEs
80(1)
3.4 MATLAB Solutions to Optimization Problems
81(5)
3.4.1 Unconstrained Optimization Problems
81(1)
3.4.2 Constrained Optimization Problems
82(2)
3.4.3 Least Squares Curve Fitting
84(2)
3.5 Laplace and z Transforms and MATLAB Solutions
86(2)
3.5.1 Laplace Transform
86(1)
3.5.2 z Transform
87(1)
3.6 Problems
88(6)
Bibliography and References
94(1)
4 Mathematical Models of Linear Control Systems 95(56)
4.1 Linear System Models of Linear Continuous Systems
96(7)
4.1.1 Transfer Function Models
96(3)
4.1.2 State Space Models
99(1)
4.1.3 State Space Models with Internal Delays
100(1)
4.1.4 Zero-pole-gain Models
100(2)
4.1.5 Transfer Function Matrices of Multivariable Systems
102(1)
4.2 Mathematical Models of Linear Discrete-time Systems
103(2)
4.2.1 Discrete-time Transfer Function Models
103(1)
4.2.2 Discrete-time State Space Models
104(1)
4.3 Equivalent Conversions of System Models
105(5)
4.3.1 Conversion Between Continuous and Discrete-time Models
105(2)
4.3.2 Converting to Transfer Function Models
107(1)
4.3.3 State Space Realization of Control Systems
108(1)
4.3.4 Balanced Realizations
108(1)
4.3.5 Minimum Realization of State Space Models
109(1)
4.3.6 Conversion between Transfer Functions and Symbolic Expressions
110(1)
4.4 Block Diagram Description and Simplification
110(11)
4.4.1 Typical Connections of Control Systems
110(4)
4.4.2 Delay Loop Processing with State Space Models
114(2)
4.4.3 Equivalent Transforms When the Nodes Are Moved
116(1)
4.4.4 Simplification of Complicated Block Diagrams
117(2)
4.4.5 Model Simplification Using An Algebraic Approach
119(2)
4.5 Model Reduction of Linear Systems
121(13)
4.5.1 Pade Approximations and Routh Approximations
122(3)
4.5.2 Pade Approximations to Models with Time Delays
125(2)
4.5.3 Sub-optimal Model Reduction to Models with Time Delays
127(4)
4.5.4 Reduction Approaches for State Space Models
131(3)
4.6 Identification of Linear Systems
134(10)
4.6.1 Identification of Discrete-time Models
134(4)
4.6.2 Order Selection in Identification
138(2)
4.6.3 Generation of Signals for Identification
140(1)
4.6.4 Identification of Continuous Systems
141(1)
4.6.5 Identification of Multivariable Systems
142(1)
4.6.6 Least Squares Recursive Identification
143(1)
4.7 Problems
144(4)
Bibliography and References
148(3)
5 Computer-Aided Analysis of Linear Control Systems 151(58)
5.1 Properties of Linear Control Systems
151(15)
5.1.1 Stability of Linear Systems
152(3)
5.1.2 Internal Stability of Feedback Control Systems
155(1)
5.1.3 Similarity Transformation of Linear Control Systems
156(1)
5.1.4 Controllability of Linear Systems
157(2)
5.1.5 Observability of Linear Systems
159(1)
5.1.6 Canonical Kalman Decompositions
160(1)
5.1.7 MATLAB Solutions to Canonical State Space Models
161(4)
5.1.8 Norms of Linear Systems
165(1)
5.2 Analytical Time Domain Responses of Linear Systems
166(9)
5.2.1 Analytical Solutions with Direct Integration Method
166(1)
5.2.2 Analytical Solutions with State Augmentation Method
167(2)
5.2.3 Analytical Solutions with Laplace and z Transforms
169(3)
5.2.4 Time Responses of Systems with Nonzero Initial Conditions
172(1)
5.2.5 Time Response Specifications of Second-order Systems
173(2)
5.3 Numerical Solutions of Time Domain Responses
175(7)
5.3.1 Step Responses and Impulse Responses
175(5)
5.3.2 Time Domain Responses for Arbitrary Inputs
180(1)
5.3.3 Responses for Systems with Nonzero Initial Conditions
181(1)
5.4 Root Locus Analysis
182(6)
5.5 Frequency Domain Analysis
188(8)
5.5.1 Frequency Domain Analysis of Single Variable Systems
189(4)
5.5.2 Stability Assessment of Feedback Systems
193(1)
5.5.3 Gain Margins and Phase Margins
194(2)
5.6 Frequency Domain Analysis of Multivariable Systems
196(7)
5.6.1 Frequency Domain Analysis of Multivariable Systems
196(2)
5.6.2 Diagonal Dominance Analysis
198(4)
5.6.3 Singular Value Plots for Multivariable Systems
202(1)
5.7 Problems
203(5)
Bibliography and References
208(1)
6 Simulink and Simulation of Nonlinear Systems 209(54)
6.1 Fundamentals of Simulink Modeling
210(7)
6.1.1 Introduction to Simulink
210(1)
6.1.2 Commonly Used Blocks in Simulink
211(5)
6.1.3 Other Commonly Used Blocksets
216(1)
6.2 Simulink Modeling and Simulation
217(9)
6.2.1 Introduction to Simulink Modeling Methodology
217(4)
6.2.2 Simulation Algorithms and Simulation Parameter Selections
221(2)
6.2.3 An Illustrative Example of Simulink Modeling
223(3)
6.3 Simulink Modeling of Various Control Systems
226(11)
6.4 Analysis and Simulation of Nonlinear Systems
237(6)
6.4.1 Modeling of Piecewise Nonlinearities
237(2)
6.4.2 Linearization of Nonlinear Systems
239(4)
6.5 Subsystem and Model Masking Methods
243(7)
6.5.1 Subsystem Creation
243(1)
6.5.2 Subsystem Masking
244(5)
6.5.3 Constructing Users' Own Block Library
249(1)
6.6 M-function, S-function and Their Applications
250(8)
6.6.1 Basic Structure of M-function Blocks
250(1)
6.6.2 Basic Structures of S-functions
251(1)
6.6.3 Examples of MATLAB S-function Programming
252(5)
6.6.4 Mask an S-Function Block
257(1)
6.7 Problems
258(3)
Bibliography and References
261(2)
7 Classical Design Approaches of Control Systems 263(56)
7.1 Design of Phase Lead-lag Compensators
264(6)
7.1.1 Lead-leg Compensators
264(2)
7.1.2 A Design Algorithm for Lead-lag Compensator
266(4)
7.2 State Space-based Controller Design Strategies
270(11)
7.2.1 State Feedback Control
270(1)
7.2.2 Linear Quadratic Optimal Regulators
271(2)
7.2.3 Pole Placement Controller Design
273(3)
7.2.4 Observer Design and Observer-based Regulators
276(5)
7.3 Optimal Controller Design
281(9)
7.3.1 Introduction to Optimal Control
281(3)
7.3.2 An Optimal Controller Design Interface
284(5)
7.3.3 Other Applications of OCD Interface
289(1)
7.4 Controller Design Interfaces in Control System Toolbox
290(6)
7.4.1 Introduction to MATLAB Controller Design Interface
290(3)
7.4.2 An Example of Parameter Automatic Tuning for Single Variable Systems
293(3)
7.5 Frequency Domain Design Methods for Multivariable Systems
296(14)
7.5.1 Diagonal Dominant and Pseudo-diagonalization
297(5)
7.5.2 Parameter Optimization Design for Multivariable Systems
302(6)
7.5.3 Optimal Controller Design with OCD Interface
308(2)
7.6 Decoupling Control of Multivariable Systems
310(4)
7.6.1 Decoupling Control with State Feedback
310(1)
7.6.2 Decoupling of State Feedback with Pole Placement
311(3)
7.7 Problems
314(3)
Bibliography and References
317(2)
8 Parameter Tuning of PID Controllers 319(40)
8.1 Introduction to PID Controller Design
320(4)
8.1.1 Continuous PID Controllers
320(2)
8.1.2 Discrete PID Controllers
322(1)
8.1.3 Variations of PID Controllers
323(1)
8.2 First-order Delay Model Approximation to Plant Models
324(4)
8.2.1 FOPDT Model by Step Responses
324(2)
8.2.2 Fitting by Frequency Domain Responses
326(1)
8.2.3 Transfer Function-based Identification
327(1)
8.2.4 Sub-optimal Reduction Method
327(1)
8.3 Parameter Tuning of PID Controllers for FOPDT Plants
328(10)
8.3.1 Ziegler-Nichols Empirical Formula
328(2)
8.3.2 Improved Ziegler-Nichols Algorithm
330(2)
8.3.3 Improved PID Control Structure and Algorithms
332(3)
8.3.4 Chien-Hrones-Reswick Parameter Tuning Algorithm
335(1)
8.3.5 Optimal PID Controller Tuning Rule
336(2)
8.4 PID Tuner - A PID Controller Design Interface
338(3)
8.5 PID Parameters Tuning for Other Plant Types
341(10)
8.5.1 PD and PID Parameter Tuning for IPD Plants
341(1)
8.5.2 PD and PID Parameter Tuning for FOLIPD Plants
342(1)
8.5.3 PD and PID Parameter Tuning for Unstable FOPDT Plants
343(1)
8.5.4 Interactive PID Controller Tuning Interface
344(3)
8.5.5 PID Controller Design and Timing
347(3)
8.5.6 Automatic Tuning Tool based on MATLAB and Simulink
350(1)
8.6 OptimPID - An Optimal PID Controller Design Interface
351(5)
8.7 Problems
356(2)
Bibliography and References
358(1)
9 Robust Control and Robust Controller Design 359(48)
9.1 Linear Quadratic Gaussian Control
360(9)
9.1.1 LQG Problems
360(1)
9.1.2 Solving LQG Problems with MATLAB
360(4)
9.1.3 LQG Control with Loop Transfer Recovery
364(5)
9.2 General Descriptions to Robust Control Problems
369(8)
9.2.1 Small Gain Theorem
369(1)
9.2.2 Structures of Robust Controllers
369(3)
9.2.3 Description of Loop Shaping Techniques
372(1)
9.2.4 MATLAB Description of Robust Control Systems
373(4)
9.3 Norm-based Robust Controller Design
377(10)
9.3.1 Design of Hinfinity and H2 Robust Controllers
377(5)
9.3.2 Other Robust Controller Design Functions
382(4)
9.3.3 Youla Parameterization
386(1)
9.4 Linear Matrix Inequality Theory and Solutions
387(10)
9.4.1 General Descriptions of Linear Matrix Inequalities
387(3)
9.4.2 MATLAB Solutions to Linear Matrix Inequality Problems
390(3)
9.4.3 Optimization Problem Solutions with YALMIP Toolbox
393(1)
9.4.4 Simultaneous Stabilization Multiple Linear Models
394(1)
9.4.5 Robust Optimal Controller Design with LMI Solvers
395(2)
9.5 Quantitative Feedback Theory and Design Methods
397(7)
9.5.1 Introduction to Quantitative Feedback Theory
397(1)
9.5.2 QFT Design Method for Single Variable Systems
398(6)
9.6 Problems
404(1)
Bibliography and References
405(2)
10 Adaptive and Intelligent Control Systems Design 407(70)
10.1 Design of Adaptive Control Systems
408(10)
10.1.1 Design and Simulation of Model Reference Adaptive Control Systems
409(2)
10.1.2 Solutions of Polynomial Diophantine Equations
411(2)
10.1.3 d-step Ahead Forecast
413(1)
10.1.4 Design of Minimum Variance Controllers
414(2)
10.1.5 Generalized Minimum Variance Control
416(2)
10.2 Simulation and Design of Model Predictive Control Systems
418(14)
10.2.1 Dynamic Matrix Control
419(1)
10.2.2 Design of Model Predictive Controllers with MATLAB
420(6)
10.2.3 Design and Simulation of Model Predictive Control for Complicated Plants
426(3)
10.2.4 Generalized Predictive Control Systems and Simulations
429(3)
10.3 Fuzzy Control and Fuzzy Logic Controller Design
432(10)
10.3.1 Fuzzy Logic and Fuzzy Inference
432(2)
10.3.2 Design of Fuzzy PD Controller
434(5)
10.3.3 Design of Fuzzy PID Controllers
439(3)
10.4 Neural Networks and Neural Network Controller Design
442(14)
10.4.1 Introduction to Neural Networks
443(1)
10.4.2 Design of PID Controller with Single Neurons
444(3)
10.4.3 PID Controller with Back-propagation Neural Networks
447(2)
10.4.4 PID Controller with Radial Basis Function-based Neural Network
449(2)
10.4.5 Design and Simulation of Neural Network Controllers
451(5)
10.5 Simulation Analysis of Iterative Learning Control
456(6)
10.5.1 Principles of Iterative Learning Control
456(2)
10.5.2 Iterative Learning Control Algorithms
458(4)
10.6 Design of Global Optimal Controllers
462(9)
10.6.1 Introduction to Genetic Algorithm
462(2)
10.6.2 Solving Global Optimization Problems with Genetic Algorithms
464(3)
10.6.3 Particle Swarm Optimization Algorithms and Applications
467(1)
10.6.4 Optimal Controller Design with Global Optimization Algorithms
468(3)
10.7 Problems
471(3)
Bibliography and References
474(3)
11 Analysis and Design of Fractional-order Systems 477(46)
11.1 Definitions and Numerical Computations in Fractional-order Calculus
478(2)
11.1.1 Definitions of Fractional-order Calculus
478(1)
11.1.2 The Relationship of Different Definitions
479(1)
11.1.3 Properties of Fractional-order Calculus
480(1)
11.2 Numerical Computations in Fractional-order Calculus
480(5)
11.2.1 Numerical Solutions with Grunwald-Letnikov Definition
481(1)
11.2.2 Numerical Solutions with Caputo Definition
482(1)
11.2.3 Mittag-Leffler Functions and Their Computations
483(2)
11.3 Solutions of Linear Fractional-order Systems
485(6)
11.3.1 Numerical Solutions of Linear Fractional-order Differential Equations
485(2)
11.3.2 Numerical Solutions of Caputo Differential Equations
487(2)
11.3.3 Some Important Laplace Transforms
489(1)
11.3.4 Analytical Solutions of Commensurate-order Linear Differential Equations
489(2)
11.3.5 Analytical Solutions of Linear Fractional-order Differential Equations
491(1)
11.4 Modeling and Analysis of Fractional-order Transfer Functions
491(12)
11.4.1 FOTF - Creation of a MATLAB Object
492(2)
11.4.2 Interconnections of FOTF Blocks
494(2)
11.4.3 Analysis of FOTF Objects
496(3)
11.4.4 Frequency Domain Analysis of FOTF Objects
499(1)
11.4.5 Time Domain Analysis of FOTF Objects
499(2)
11.4.6 Root Locus for Commensurate-order Systems
501(1)
11.4.7 State Space Models of Commensurate-order Systems
502(1)
11.5 Approximation and Reduction of Fractional-order Systems
503(5)
11.5.1 Oustaloup Filter for Fractional-order Differentiators
503(2)
11.5.2 Approximations of Fractional-order Controllers
505(1)
11.5.3 Optimal Reduction Algorithm for Fractional-order Models
506(2)
11.6 Simulation Methods for Complicated Fractional-order Systems
508(5)
11.6.1 Simulation with Numerical Laplace Transform
508(2)
11.6.2 Block Diagram Modeling and Simulation of Linear Fractional-order Systems
510(2)
11.6.3 Block Diagram Modeling and Simulation of Nonlinear Fractional-order Systems
512(1)
11.7 Design of Optimal Fractional-order PID Controllers
513(6)
11.7.1 Optimal Design of PIλDµ Controllers
513(5)
11.7.2 OptimFOPID - An Optimal Fractional-order PID Controller Design Interface
518(1)
11.8 Problems
519(2)
Bibliography and References
521(2)
12 Hardware-in-the-loop Simulation and Real-time Control 523(16)
12.1 Introduction to dSPACE and Commonly Used Blocks
523(2)
12.2 Introduction to Quanser System and Its Blocks
525(3)
12.2.1 Introduction to Commonly Used Blocks in Quanser
525(1)
12.2.2 Brief Introduction to Plants in Quanser Rotary Series
526(2)
12.3 An Example of Hardware-in-the-loop Simulation and Real-time Control
528(6)
12.3.1 Mathematical Description of the Plant Model
528(2)
12.3.2 Real-time Experiments with Quanser
530(2)
12.3.3 Real-time Experiments with dSPACE
532(2)
12.4 Low-cost Realizations of Hardware-in-the-loop Simulation
534(4)
12.4.1 Arduino Interface Installation and Settings
534(1)
12.4.2 Applications of Arduino Control
535(2)
12.4.3 The MESA Box
537(1)
12.5 Problems
538(1)
Bibliography and References
538(1)
Appendix A Some Practical Plant Models 539(5)
A.1 Well-known Benchmark Problems
539(2)
A.1.1 Control of the F-14 Aircraft Model
539(1)
A.1.2 ACC Benchmark Problem
540(1)
A.2 Other Engineering Models
541(2)
A.2.1 Servo Control System Model
541(1)
A.2.2 Mathematical Model of Inverted Pendulum
542(1)
A.2.3 AIRC Model
543(1)
A.3 Problems
543(1)
Bibliography and References 544(1)
Index of Functions 545(6)
Index 551