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E-raamat: Simulation of Dynamic Systems with MATLAB® and Simulink®

(University of Central Florida, Orlando, USA), (University of Central Florida, Orlando, USA)
  • Formaat: 852 pages
  • Ilmumisaeg: 02-Feb-2018
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781498787789
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Simulation of Dynamic Systems with MATLAB® and Simulink®Suurem pilt
  • Formaat: 852 pages
  • Ilmumisaeg: 02-Feb-2018
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781498787789
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Continuous-system simulation is an increasingly important tool for optimizing the performance of real-world systems. The book presents an integrated treatment of continuous simulation with all the background and essential prerequisites in one setting. It features updated chapters and two new sections on Black Swan and the Stochastic Information Packet (SIP) and Stochastic Library Units with Relationships Preserved (SLURP) Standard. The new edition includes basic concepts, mathematical tools, and the common principles of various simulation models for different phenomena, as well as an abundance of case studies, real-world examples, homework problems, and equations to develop a practical understanding of concepts.

Arvustused

"The authors provide a comprehensive set of MATLAB and Simulink-based mechanical systems examples. Derivations of concepts are followed by concrete examples using various numerical methods. Detailed solutions to the Exercises at the end of each chapter provide the instructor with a fairly wide array of selections for homework assignments. This is a useful textbook on numerical methods using MATLAB and Simulink for advanced undergraduates, as well as graduate students."

George Shoane, Rutgers University, New Jersey, USA

"An excellent combination of mathematical rigor combined with in-depth numerical analysis, all spun together with excellent case studies."

Paul McKenna, University of Glasgow, UK

"This updated edition of the book allows the reader/student/engineer to learn the fundamental concepts in dynamic systems modeling and simulation in a step-by-step manner. The authors have meticulously used MATLAB and Simulink in simulating a wide variety of dynamic systems. This book can be used as a self-study book and hence a definite must have for anyonefrom a novice to an experienced engineerinterested in understanding and learning modeling and simulation using MATLAB/Simulink."

Rajesh Joseph Abraham, Indian Institute of Space Science & Technology, India

"This book is both a great textbook and a great reference on the subject of simulation of dynamics systems. It provides an applied and practical presentation of the MATLAB and Simulink tools and uses them to implement and apply the concepts presented in the book. The presentation of concepts is methodical and clear, with lots of industry relevant examples. The material covered provides a great foundation for developing and understanding simulations of dynamic systems. I recommend this book to both novices and experts, as it covers basic to advanced simulation topics in a clear and accessible way. This book has been a great reference to have in the field to build from and apply its concept to industry relevant simulation problems. The third edition updated the Simulink diagrams, which improve readability and consistency with what the Simulink user may see on his or her screen.

Danilo Viazzo, Engineering Consultant, USA

"The book is at its 3rd edition, meaning that it has survived the field tests from academia and industry. The book starts with the need for mathematical modeling of real-life systems, and moves on to the corresponding formulation of continuous-time systems and then their discrete-time approximation. The theory of linear system analysis then follows to lay the theoretical toolkit for system analysis and simulation. Next, the MATLAB and Simulink knowledge kick in to furnish the practical toolkit for an undergraduate/postgraduate/practitioner to start visualizing the dynamics of the systems on a computer console. Such a bottom-up approach to system modeling and simulation provides a solid and manageable path to acquire necessary and important skills for the design and engineering of various electrical and mechanical systems. Overall, the book serves as a good introductory material to system analysis, modeling, and simulation, with a good balance between theory and practice."

Ngai Wong, The University of Hong Kong

Foreword xiii
Preface xv
About the Authors xix
Chapter 1 Mathematical Modeling 1(28)
1.1 Introduction
1(3)
1.1.1 Importance of Models
1(3)
1.2 Derivation of A Mathematical Model
4(6)
1.3 Difference Equations
10(9)
1.4 First Look at Discrete-Time Systems
19(3)
1.4.1 Inherently Discrete-Time Systems
19(3)
1.5 Case Study: Population Dynamics (Single Species)
22(7)
Chapter 2 Continuous-Time Systems 29(62)
2.1 Introduction
29(1)
2.2 First-Order Systems
29(7)
2.2.1 Step Response of First-Order Systems
30(6)
2.3 Second-Order Systems
36(9)
2.3.1 Conversion of Two First-Order Equations to a Second-Order Model
41(4)
2.4 Simulation Diagrams
45(9)
2.4.1 Systems of Equations
51(3)
2.5 Higher-Order Systems
54(3)
2.6 State Variables
57(9)
2.6.1 Conversion from Linear State Variable Form to Single Input-Single Output Form
62(1)
2.6.2 General Solution of the State Equations
63(3)
2.7 Nonlinear Systems
66(19)
2.7.1 Friction
68(3)
2.7.2 Dead Zone and Saturation
71(1)
2.7.3 Backlash
72(1)
2.7.4 Hysteresis
72(4)
2.7.5 Quantization
76(1)
2.7.6 Sustained Oscillations and Limit Cycles
77(8)
2.8 Case Study: Submarine Depth Control System
85(6)
Chapter 3 Elementary Numerical Integration 91(64)
3.1 Introduction
91(1)
3.2 Discrete-Time System Approximation of a Continuous First-Order System
92(6)
3.3 Euler Integration
98(6)
3.3.1 Explicit Euler Integration
99(1)
3.3.2 Implicit Euler Integration
100(4)
3.4 Trapezoidal Integration
104(8)
3.5 Discrete Approximation of Nonlinear First-Order Systems
112(4)
3.6 Discrete State Equations
116(11)
3.7 Improvements to Euler Integration
127(19)
3.7.1 Improved Euler Integration
127(4)
3.7.2 Modified Euler Integration
131(1)
3.7.3 Discrete-Time System Matrices
132(14)
3.8 Case Study: Vertical Ascent of a Diver
146(9)
Chapter 4 Linear Systems Analysis 155(194)
4.1 Introduction
155(1)
4.2 Laplace Transform
155(18)
4.2.1 Properties of the Laplace Transform
156(7)
4.2.2 Inverse Laplace Transform
163(1)
4.2.3 Laplace Transform of the System Response
164(2)
4.2.4 Partial Fraction Expansion
166(7)
4.3 Transfer Function
173(21)
4.3.1 Impulse Function
173(1)
4.3.2 Relationship between Unit Step Function and Unit Impulse Function
173(2)
4.3.3 Impulse Response
175(4)
4.3.4 Relationship between Impulse Response and Transfer Function
179(3)
4.3.5 Systems with Multiple Inputs and Outputs
182(8)
4.3.6 Transformation from State Variable Model to Transfer Function
190(4)
4.4 Stability of Linear Time Invariant Continuous-Time Systems
194(12)
4.4.1 Characteristic Polynomial
195(5)
4.4.2 Feedback Control System
200(6)
4.5 Frequency Response of LTI Continuous-Time Systems
206(16)
4.5.1 Stability of Linear Feedback Control Systems Based on Frequency Response
216(6)
4.6 z-Transform
222(20)
4.6.1 Discrete-Time Impulse Function
226(6)
4.6.2 Inverse z-Transform
232(1)
4.6.3 Partial Fraction Expansion
233(9)
4.7 z-Domain Transfer Function
242(25)
4.7.1 Nonzero Initial Conditions
243(2)
4.7.2 Approximating Continuous-Time System Transfer Functions
245(5)
4.7.3 Simulation Diagrams and State Variables
250(6)
4.7.4 Solution of Linear Discrete-Time State Equations
256(5)
4.7.5 Weighting Sequence (Impulse Response Function)
261(6)
4.8 Stability of LTI Discrete-Time Systems
267(13)
4.8.1 Complex Poles of H(z)
271(9)
4.9 Frequency Response of Discrete-Time Systems
280(20)
4.9.1 Steady-State Sinusoidal Response
280(2)
4.9.2 Properties of the Discrete-Time Frequency Response Function
282(5)
4.9.3 Sampling Theorem
287(6)
4.9.4 Digital Filters
293(7)
4.10 Control System Toolbox
300(19)
4.10.1 Transfer Function Models
301(1)
4.10.2 State-Space Models
302(1)
4.10.3 State-Space/Transfer Function Conversion
303(2)
4.10.4 System Interconnections
305(2)
4.10.5 System Response
307(2)
4.10.6 Continuous-/Discrete-Time System Conversion
309(2)
4.10.7 Frequency Response
311(2)
4.10.8 Root Locus
313(6)
4.11 Case Study: Longitudinal Control of an Aircraft
319(19)
4.11.1 Digital Simulation of Aircraft Longitudinal Dynamics
333(2)
4.11.2 Simulation of State Variable Model
335(3)
4.12 Case Study: Notch Filter for Electrocardiograph Waveform
338(11)
4.12.1 Multinotch Filters
339(10)
Chapter 5 Simulink® 349(126)
5.1 Introduction
349(1)
5.2 Building a Simulink Model
349(8)
5.2.1 The Simulink Library
349(4)
5.2.2 Running a Simulink Model
353(4)
5.3 Simulation of Linear Systems
357(14)
5.3.1 Transfer Fcn Block
357(6)
5.3.2 State-Space Block
363(8)
5.4 Algebraic Loops
371(9)
5.4.1 Eliminating Algebraic Loops
373(2)
5.4.2 Algebraic Equations
375(5)
5.5 More Simulink Blocks
380(14)
5.5.1 Discontinuities
385(1)
5.5.2 Friction
386(1)
5.5.3 Dead Zone and Saturation
387(2)
5.5.4 Backlash
389(1)
5.5.5 Hysteresis
389(2)
5.5.6 Quantization
391(3)
5.6 Subsystems
394(8)
5.6.1 PHYSBE
395(1)
5.6.2 Car-Following Subsystem
396(2)
5.6.3 Subsystem Using Fcn Blocks
398(4)
5.7 Discrete-Time Systems
402(20)
5.7.1 Simulation of an Inherently Discrete-Time System
403(3)
5.7.2 Discrete-Time Integrator
406(3)
5.7.3 Centralized Integration
409(3)
5.7.4 Digital Filters
412(2)
5.7.5 Discrete-Time Transfer Function
414(8)
5.8 MATLAB and Simulink Interface
422(9)
5.9 Hybrid Systems: Continuous- and Discrete-Time Components
431(4)
5.10 Monte Carlo Simulation
435(13)
5.10.1 Monte Carlo Simulation Requiring Solution of a Mathematical Model
439(9)
5.11 Case Study: Pilot Ejection
448(5)
5.12 Case Study: Kalman Filtering
453(16)
5.12.1 Continuous-Time Kalman Filter
453(1)
5.12.2 Steady-State Kalman Filter
454(1)
5.12.3 Discrete-Time Kalman Filter
454(1)
5.12.4 Simulink Simulations
455(13)
5.12.5 Summary
468(1)
5.13 Case Study: Cascaded Tanks with Flow Logic Control
469(6)
Chapter 6 Intermediate Numerical Integration 475(106)
6.1 Introduction
475(1)
6.2 Runge-Kutta (RK) (One-Step Methods)
475(25)
6.2.1 Taylor Series Method
476(1)
6.2.2 Second-Order Runge-Kutta Method
477(2)
6.2.3 Truncation Errors
479(5)
6.2.4 High-Order Runge-Kutta Methods
484(2)
6.2.5 Linear Systems: Approximate Solutions Using RK Integration
486(2)
6.2.6 Continuous-Time Models with Polynomial Solutions
488(2)
6.2.7 Higher-Order Systems
490(10)
6.3 Adaptive Techniques
500(12)
6.3.1 Repeated RK with Interval Halving
500(5)
6.3.2 Constant Step Size (T = 1 min)
505(1)
6.3.3 Adaptive Step Size (Initial T = 1 min)
505(1)
6.3.4 RK-Fehlberg
505(7)
6.4 Multistep Methods
512(11)
6.4.1 Explicit Methods
513(2)
6.4.2 Implicit Methods
515(3)
6.4.3 Predictor-Corrector Methods
518(5)
6.5 Stiff Systems
523(23)
6.5.1 Stiffness Property in First-Order System
524(2)
6.5.2 Stiff Second-Order System
526(3)
6.5.3 Approximating Stiff Systems with Lower-Order Nonstiff System Models
529(17)
6.6 Lumped Parameter Approximation of Distributed Parameter Systems
546(9)
6.6.1 Nonlinear Distributed Parameter System
550(5)
6.7 Systems with Discontinuities
555(18)
6.7.1 Physical Properties and Constant Forces Acting on the Pendulum Bob
563(10)
6.8 Case Study: Spread of an Epidemic
573(8)
Chapter 7 Simulation Tools 581(102)
7.1 Introduction
581(1)
7.2 Steady-State Solver
582(14)
7.2.1 Trim Function
584(2)
7.2.2 Equilibrium Point for a Nonautonomous System
586(10)
7.3 Optimization of Simulink Models
596(34)
7.3.1 Gradient Vector
605(2)
7.3.2 Optimizing Multiparameter Objective Functions Requiring Simulink Models
607(3)
7.3.3 Parameter Identification
610(1)
7.3.4 Example of a Simple Gradient Search
611(9)
7.3.5 Optimization of Simulink Discrete Time System Models
620(10)
7.4 Linearization
630(29)
7.4.1 Deviation Variables
631(8)
7.4.2 Linearization of Nonlinear Systems in State Variable Form
639(4)
7.4.3 Linmod Function
643(5)
7.4.4 Multiple Linearized Models for a Single System
648(11)
7.5 Adding Blocks to The Simulink Library Browser
659(6)
7.5.1 Introduction
659(6)
7.5.2 Summary
665(1)
7.6 Simulation Acceleration
665(3)
7.6.1 Introduction
665(2)
7.6.2 Profiler
667(1)
7.6.3 Summary
668(1)
7.7 Black Swans
668(9)
7.7.1 Introduction
668(1)
7.7.2 Modeling Rare Events
668(1)
7.7.3 Measurement of Portfolio Risk
669(4)
7.7.4 Exposing Black Swans
673(3)
7.7.4.1 Percent Point Functions (PPFs)
673(1)
7.7.4.2 Stochastic Optimization
673(3)
7.7.5 Summary
676(1)
7.7.6 Acknowledgements
676(1)
7.7.7 References
676(1)
7.7.8 Appendix-Mathematical Properties of the Log-Stable Distribution
676(1)
7.8 The SIPmath Standard
677(6)
7.8.1 Introduction
677(1)
7.8.2 Standard Specification
677(1)
7.8.3 SIP Details
678(1)
7.8.4 SLURP Details
678(1)
7.8.5 SIPs/SLURPs and MATLAB
679(1)
7.8.6 Summary
680(1)
7.8.7 Appendix
681(1)
7.8.8 References
682(1)
Chapter 8 Advanced Numerical Integration 683(134)
8.1 Introduction
683(1)
8.2 Dynamic Errors (Characteristic Roots, Transfer Function)
683(31)
8.2.1 Discrete-Time Systems and the Equivalent Continuous-Time Systems
684(3)
8.2.2 Characteristic Root Errors
687(10)
8.2.3 Transfer Function Errors
697(7)
8.2.4 Asymptotic Formulas for Multistep Integration Methods
704(4)
8.2.5 Simulation of Linear System with Transfer Function H(s)
708(6)
8.3 Stability of Numerical Integrators
714(24)
8.3.1 Adams-Bashforth Numerical Integrators
714(8)
8.3.2 Implicit Integrators
722(4)
8.3.3 Runga-Kutta (RK) Integration
726(12)
8.4 Multirate Integration
738(28)
8.4.1 Procedure for Updating Slow and Fast States: Master/Slave = RK-4/RK-4
742(1)
8.4.2 Selection of Step Size Based on Stability
743(2)
8.4.3 Selection of Step Size Based on Dynamic Accuracy
745(3)
8.4.4 Analytical Solution for State Variables
748(2)
8.4.5 Multirate Integration of Aircraft Pitch Control System
750(3)
8.4.6 Nonlinear Dual Speed Second-Order System
753(7)
8.4.7 Multirate Simulation of Two-Tank System
760(3)
8.4.8 Simulation Trade-Offs with Multirate Integration
763(3)
8.5 Real-Time Simulation
766(24)
8.5.1 Numerical Integration Methods Compatible with Real-Time Operation
769(1)
8.5.2 RK-1 (Explicit Euler)
770(1)
8.5.3 RK-2 (Improved Euler)
771(1)
8.5.4 RK-2 (Modified Euler)
771(1)
8.5.5 RK-3 (Real-Time Incompatible)
771(1)
8.5.6 RK-3 (Real-Time Compatible)
772(1)
8.5.7 RK-4 (Real-Time Incompatible)
772(1)
8.5.8 Multistep Integration Methods
772(2)
8.5.9 Stability of Real-Time Predictor-Corrector Method
774(2)
8.5.10 Extrapolation of Real-Time Inputs
776(7)
8.5.11 Alternate Approach to Real-Time Compatibility: Input Delay
783(7)
8.6 Additional Methods of Approximating Continuous-Time System Models
790(13)
8.6.1 Sampling and Signal Reconstruction
790(6)
8.6.2 First-Order Hold Signal Reconstruction
796(1)
8.6.3 Matched Pole-Zero Method
796(3)
8.6.4 Bilinear Transform with Prewarping
799(4)
8.7 Case Study: Lego Mindstorms™ NXT
803(14)
8.7.1 Introduction
803(2)
8.7.2 Requirements and Installation
805(1)
8.7.3 Noisy Model
806(4)
8.7.4 Filtered Model
810(5)
8.7.5 Summary
815(2)
References 817(4)
Index 821
Dr. Harold Klee received his Ph.D. in systems science from Polytechnic Institute of Brooklyn in 1972, his MS in systems engineering from Case Institute of Technology in 1968, and his BSME from The Cooper Union in 1965. Dr. Klee was a faculty member in the College of Engineering at the University of Central Florida (UCF) from 1972, until his retirement from UCF in 2009. During his tenure there, he was a five-time recipient of the colleges Outstanding Teacher Award. He has been instrumental in the development of simulation courses in both the undergraduate and graduate curricula. A charter member of the Core Faculty of the Institute of Simulation and Training, which is responsible for developing the interdisciplinary MS and Ph.D. programs in simulation at UCF, Dr. Klee has served as the director of the UCF Driving Simulation Lab for more than 15 years, and he has been Editor-In- Chief of the Modeling and Simulation magazine for three years.









Dr. Randal Allen has over 25 years of industry experience and is currently the Chief Scientist for Lone Star Analysis, where he is responsible for creating and applying new technologies to maintain competitive advantage in the marketplace. His experience includes 6DOF aerodynamic simulation, modeling, analysis, design, integration, and test of navigation, guidance, and control systems. He is an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA). He is certified as a modeling and simulation professional (CMSP) by the National Training and Simulation Association (NTSA). Dr. Allens academic background includes a Ph.D. in Mechanical Engineering from the University of Central Florida, an Engineers Degree in Aeronautical and Astronautical Engineering from Stanford University, an M.S. in Applied Mathematics and a B.S. in Engineering Physics, both from the University of Illinois (Urbana-Champaign). He also serves as an Adjunct Professor in the Mechanical, Materials, and Aerospace Engineering (MMAE) department at the University of Central Florida (UCF) in Orlando, Florida.
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