Simulation of dynamic systems with MATLAB® and Simulink® /

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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 s...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Klee, Harold (Autor), Allen, Randal, 1964- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press, 2017.
Edición:Third edition.
Temas:
SIMULINK.
MATLAB.
SIMULINK
MATLAB
Computer simulation.
Digital computer simulation.
Systems & Controls.
Vibration.
Mathematical Modeling.
ENGnetBASE.
MATHnetBASE.
ElectricalEngineeringnetBASE.
MechanicalEngineeringnetBASE.
SCI-TECHnetBASE.
STMnetBASE.
Simulation par ordinateur.
simulation.
TECHNOLOGY & ENGINEERING > Electrical.
TECHNOLOGY & ENGINEERING > Mechanical.
Digital computer simulation
Computer simulation
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Dedication
  • Contents
  • Foreword
  • Preface
  • About the Authors
  • Chapter 1: Mathematical Modeling
  • 1.1 Introduction
  • 1.1.1 Importance of Models
  • 1.2 Derivation of A Mathematical Model
  • 1.3 Difference Equations
  • 1.4 First Look at Discrete-Time Systems
  • 1.4.1 Inherently Discrete-Time Systems
  • 1.5 Case Study: Population Dynamics (Single Species)
  • Chapter 2: Continuous-Time Systems
  • 2.1 Introduction
  • 2.2 First-Order Systems
  • 2.2.1 Step Response of First-Order Systems
  • 2.3 Second-Order Systems
  • 2.3.1 Conversion of Two First-Order Equations to a Second-Order Model
  • 2.4 Simulation Diagrams
  • 2.4.1 Systems of Equations
  • 2.5 Higher-Order Systems
  • 2.6 State Variables
  • 2.6.1 Conversion from Linear State Variable Form to Single Input-Single Output Form
  • 2.6.2 General Solution of the State Equations
  • 2.7 Nonlinear Systems
  • 2.7.1 Friction
  • 2.7.2 Dead Zone and Saturation
  • 2.7.3 Backlash
  • 2.7.4 Hysteresis
  • 2.7.5 Quantization
  • 2.7.6 Sustained Oscillations and Limit Cycles
  • 2.8 Case Study: Submarine Depth Control System
  • Chapter 3: Elementary Numerical Integration
  • 3.1 Introduction
  • 3.2 Discrete-Time System Approximation of a Continuous First-Order System
  • 3.3 Euler Integration
  • 3.3.1 Explicit Euler Integration
  • 3.3.2 Implicit Euler Integration
  • 3.4 Trapezoidal Integration
  • 3.5 Discrete Approximation of Nonlinear First-Order Systems
  • 3.6 Discrete State Equations
  • 3.7 Improvements to Euler Integration
  • 3.7.1 Improved Euler Integration
  • 3.7.2 Modified Euler Integration
  • 3.7.3 Discrete-Time System Matrices
  • 3.8 Case Study: Vertical Ascent of a Diver
  • Chapter 4: Linear Systems Analysis
  • 4.1 Introduction
  • 4.2 Laplace Transform
  • 4.2.1 Properties of the Laplace Transform
  • 4.2.2 Inverse Laplace Transform.
  • 4.2.3 Laplace Transform of the System Response
  • 4.2.4 Partial Fraction Expansion
  • 4.3 Transfer Function
  • 4.3.1 Impulse Function
  • 4.3.2 Relationship between Unit Step Function and Unit Impulse Function
  • 4.3.3 Impulse Response
  • 4.3.4 Relationship between Impulse Response and Transfer Function
  • 4.3.5 Systems with Multiple Inputs and Outputs
  • 4.3.6 Transformation from State Variable Model to Transfer Function
  • 4.4 Stability of Linear Time Invariant Continuous-Time Systems
  • 4.4.1 Characteristic Polynomial
  • 4.4.2 Feedback Control System
  • 4.5 Frequency Response of LTI Continuous-Time Systems
  • 4.5.1 Stability of Linear Feedback Control Systems Based on Frequency Response
  • 4.6 z-Transform
  • 4.6.1 Discrete-Time Impulse Function
  • 4.6.2 Inverse z-Transform
  • 4.6.3 Partial Fraction Expansion
  • 4.7 z-Domain Transfer Function
  • 4.7.1 Nonzero Initial Conditions
  • 4.7.2 Approximating Continuous-Time System Transfer Functions
  • 4.7.3 Simulation Diagrams and State Variables
  • 4.7.4 Solution of Linear Discrete-Time State Equations
  • 4.7.5 Weighting Sequence (Impulse Response Function)
  • 4.8 Stability of LTI Discrete-Time Systems
  • 4.8.1 Complex Poles of H(z)
  • 4.9 Frequency Response of Discrete-Time Systems
  • 4.9.1 Steady-State Sinusoidal Response
  • 4.9.2 Properties of the Discrete-Time Frequency Response Function
  • 4.9.3 Sampling Theorem
  • 4.9.4 Digital Filters
  • 4.10 Control System Toolbox
  • 4.10.1 Transfer Function Models
  • 4.10.2 State-Space Models
  • 4.10.3 State-Space/Transfer Function Conversion
  • 4.10.4 System Interconnections
  • 4.10.5 System Response
  • 4.10.6 Continuous-/Discrete-Time System Conversion
  • 4.10.7 Frequency Response
  • 4.10.8 Root Locus
  • 4.11 Case Study: Longitudinal Control of an Aircraft
  • 4.11.1 Digital Simulation of Aircraft Longitudinal Dynamics.
  • 4.11.2 Simulation of State Variable Model
  • 4.12 Case Study: Notch Filter for Electrocardiograph Waveform
  • 4.12.1 Multinotch Filters
  • Chapter 5: Simulink®
  • 5.1 Introduction
  • 5.2 Building a Simulink Model
  • 5.2.1 The Simulink Library
  • 5.2.2 Running a Simulink Model
  • 5.3 Simulation of Linear Systems
  • 5.3.1 Transfer Fcn Block
  • 5.3.2 State-Space Block
  • 5.4 Algebraic Loops
  • 5.4.1 Eliminating Algebraic Loops
  • 5.4.2 Algebraic Equations
  • 5.5 More Simulink Blocks
  • 5.5.1 Discontinuities
  • 5.5.2 Friction
  • 5.5.3 Dead Zone and Saturation
  • 5.5.4 Backlash
  • 5.5.5 Hysteresis
  • 5.5.6 Quantization
  • 5.6 Subsystems
  • 5.6.1 PHYSBE
  • 5.6.2 Car-Following Subsystem
  • 5.6.3 Subsystem Using Fcn Blocks
  • 5.7 Discrete-Time Systems
  • 5.7.1 Simulation of an Inherently Discrete-Time System
  • 5.7.2 Discrete-Time Integrator
  • 5.7.3 Centralized Integration
  • 5.7.4 Digital Filters
  • 5.7.5 Discrete-Time Transfer Function
  • 5.8 MATLAB and Simulink Interface
  • 5.9 Hybrid Systems: Continuous- and Discrete-Time Components
  • 5.10 Monte Carlo Simulation
  • 5.10.1 Monte Carlo Simulation Requiring Solution of a Mathematical Model
  • 5.11 Case Study: Pilot Ejection
  • 5.12 Case Study: Kalman Filtering
  • 5.12.1 Continuous-Time Kalman Filter
  • 5.12.2 Steady-State Kalman Filter
  • 5.12.3 Discrete-Time Kalman Filter
  • 5.12.4 Simulink Simulations
  • 5.12.5 Summary
  • 5.13 Case Study: Cascaded Tanks with Flow Logic Control
  • Chapter 6: Intermediate Numerical Integration
  • 6.1 Introduction
  • 6.2 Runge-Kutta (RK) (One-Step Methods)
  • 6.2.1 Taylor Series Method
  • 6.2.2 Second-Order Runge-Kutta Method
  • 6.2.3 Truncation Errors
  • 6.2.4 High-Order Runge-Kutta Methods
  • 6.2.5 Linear Systems: Approximate Solutions Using RK Integration
  • 6.2.6 Continuous-Time Models with Polynomial Solutions
  • 6.2.7 Higher-Order Systems
  • 6.3 Adaptive Techniques.
  • 6.3.1 Repeated RK with Interval Halving
  • 6.3.2 Constant Step Size (T = 1 min)
  • 6.3.3 Adaptive Step Size (Initial T = 1 min)
  • 6.3.4 RK-Fehlberg
  • 6.4 Multistep Methods
  • 6.4.1 Explicit Methods
  • 6.4.2 Implicit Methods
  • 6.4.3 Predictor-Corrector Methods
  • 6.5 Stiff Systems
  • 6.5.1 Stiffness Property in First-Order System
  • 6.5.2 Stiff Second-Order System
  • 6.5.3 Approximating Stiff Systems with Lower-Order Nonstiff System Models
  • 6.6 Lumped Parameter Approximation of Distributed Parameter Systems
  • 6.6.1 Nonlinear Distributed Parameter System
  • 6.7 Systems with Discontinuities
  • 6.7.1 Physical Properties and Constant Forces Acting on the Pendulum Bob
  • 6.8 Case Study: Spread of an Epidemic
  • Chapter 7: Simulation Tools
  • 7.1 Introduction
  • 7.2 Steady-State Solver
  • 7.2.1 Trim Function
  • 7.2.2 Equilibrium Point For a Nonautonomous System
  • 7.3 Optimization of Simulink Models
  • 7.3.1 Gradient Vector
  • 7.3.2 Optimizing Multiparameter Objective Functions Requiring Simulink Models
  • 7.3.3 Parameter Identification
  • 7.3.4 Example of a Simple Gradient Search
  • 7.3.5 Optimization of Simulink Discrete-Time System Models
  • 7.4 Linearization
  • 7.4.1 Deviation Variables
  • 7.4.2 Linearization of Nonlinear Systems in State Variable Form
  • 7.4.3 Linmod Function
  • 7.4.4 Multiple Linearized Models for a Single System
  • 7.5 Adding Blocks to The Simulink Library Browser
  • 7.5.1 Introduction
  • 7.5.2 Summary
  • 7.6 Simulation Acceleration
  • 7.6.1 Introduction
  • 7.6.2 Profiler
  • 7.6.3 Summary
  • 7.7 Black Swans
  • 7.7.1 Introduction
  • 7.7.2 Modeling Rare Events
  • 7.7.3 Measurement of Portfolio Risk
  • 7.7.4 Exposing Black Swans
  • 7.7.4.1 Percent Point Functions (PPFs)
  • 7.7.4.2 Stochastic Optimization
  • 7.7.5 Summary
  • 7.7.6 Acknowledgements
  • 7.7.7 References.
  • 7.7.8 Appendix-Mathematical Properties of the Log-Stable Distribution
  • 7.8 The SIPmath Standard
  • 7.8.1 Introduction
  • 7.8.2 Standard Specification
  • 7.8.3 SIP Details
  • 7.8.4 SLURP Details
  • 7.8.5 SIPs/SLURPs and MATLAB
  • 7.8.6 Summary
  • 7.8.7 Appendix
  • 7.8.8 References
  • Chapter 8: Advanced Numerical Integration
  • 8.1 Introduction
  • 8.2 Dynamic Errors (Characteristic Roots, Transfer Function)
  • 8.2.1 Discrete-Time Systems and the Equivalent Continuous-Time Systems
  • 8.2.2 Characteristic Root Errors
  • 8.2.3 Transfer Function Errors
  • 8.2.4 Asymptotic Formulas for Multistep Integration Methods
  • 8.2.5 Simulation of Linear System with Transfer Function H(s)
  • 8.3 Stability of Numerical Integrators
  • 8.3.1 Adams-Bashforth Numerical Integrators
  • 8.3.2 Implicit Integrators
  • 8.3.3 Runga-Kutta (RK) Integration
  • 8.4 Multirate Integration
  • 8.4.1 Procedure for Updating Slow and Fast States: Master/Slave = RK-4/RK-4
  • 8.4.2 Selection of Step Size Based on Stability
  • 8.4.3 Selection of Step Size Based on Dynamic Accuracy
  • 8.4.4 Analytical Solution for State Variables
  • 8.4.5 Multirate Integration of Aircraft Pitch Control System
  • 8.4.6 Nonlinear Dual Speed Second-Order System
  • 8.4.7 Multirate Simulation of Two-Tank System
  • 8.4.8 Simulation Trade-Offs with Multirate Integration
  • 8.5 Real-Time Simulation
  • 8.5.1 Numerical Integration Methods Compatible with Real-Time Operation
  • 8.5.2 RK-1 (Explicit Euler)
  • 8.5.3 RK-2 (Improved Euler)
  • 8.5.4 RK-2 (Modified Euler)
  • 8.5.5 RK-3 (Real-Time Incompatible)
  • 8.5.6 RK-3 (Real-Time Compatible)
  • 8.5.7 RK-4 (Real-Time Incompatible)
  • 8.5.8 Multistep Integration Methods
  • 8.5.9 Stability of Real-Time Predictor-Corrector Method
  • 8.5.10 Extrapolation of Real-Time Inputs
  • 8.5.11 Alternate Approach to Real-Time Compatibility: Input Delay.

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