Simulation of dynamic systems with MATLAB and Simulink
著者
書誌事項
Simulation of dynamic systems with MATLAB and Simulink
CRC Press, Taylor & Francis, c2011
2nd ed
- : hbk
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注記
Includes bibliographical references (p. 781-783) and index
内容説明・目次
内容説明
"... a seminal text covering the simulation design and analysis of a broad variety of systems using two of the most modern software packages available today. ... particularly adept [at] enabling students new to the field to gain a thorough understanding of the basics of continuous simulation in a single semester, and [also provides] a more advanced treatment of the subject for researchers and simulation professionals."
-From the Foreword by Chris Bauer, PhD, PE, CMSP
Continuous-system simulation is an increasingly important tool for optimizing the performance of real-world systems, and a massive transformation has occurred in the application of simulation in fields ranging from engineering and physical sciences to medicine, biology, economics, and applied mathematics. As with most things, simulation is best learned through practice-but explosive growth in the field requires a new learning approach.
A response to changes in the field, Simulation of Dynamic Systems with MATLAB (R) and Simulink (R), Second Edition has been extensively updated to help readers build an in-depth and intuitive understanding of basic concepts, mathematical tools, and the common principles of various simulation models for different phenomena.
Includes an abundance of case studies, real-world examples, homework problems, and equations to develop a practical understanding of concepts
Accomplished experts Harold Klee and Randal Allen take readers through a gradual and natural progression of important topics in simulation, introducing advanced concepts only after they construct complete examples using fundamental methods. Presented exercises incorporate MATLAB (R) and Simulink (R)-including access to downloadable M-files and model files-enabling both students and professionals to gain experience with these industry-standard tools and more easily design, implement, and adjust simulation models in their particular field of study.
More universities are offering courses-as well as masters and Ph.D programs-in both continuous-time and discrete-time simulation, promoting a new interdisciplinary focus that appeals to undergraduates and beginning graduates from a wide range of fields. Ideal for such courses, this classroom-tested introductory text presents a flexible, multifaceted approach through which simulation can play a prominent role in validating system design and training personnel involved.
目次
Mathematical Modeling
Derivation of a Mathematical Model
Difference Equations
First Look at Discrete-Time Systems
Case Study: Population Dynamics (Single Species)
Continuous-Time Systems
First-Order Systems
Second-Order Systems
Simulation Diagrams
Higher-Order Systems
State Variables
Nonlinear Systems
Case Study: Submarine Depth Control System
Elementary Numerical Integration
Discrete-Time System Approximation of a Continuous-Time Integrator
Euler Integration
Trapezoidal Integration
Numerical Integration of First-Order and Higher Continuous-Time Systems
Improvements to Euler Integration
Case Study: Vertical Ascent of a Diver
Linear Systems Analysis
Laplace Transform
Transfer Function
Stability of Linear Time Invariant Continuous-Time Systems
Frequency Response of LTI Continuous-Time Systems
z-Transform
z-Domain Transfer Function
Stability of LTI Discrete-Time Systems
Frequency Response of Discrete-Time Systems
Control System Toolbox
Case Study: Longitudinal Control of an Aircraft
Case Study: Notch Filter for Electrocardiograph Waveform
Simulink (R)
Building a Simulink (R) Model
Simulation of Linear Systems
Algebraic Loops
More Simulink (R) Blocks
Subsystems
Discrete-Time Systems
MATLAB (R) and Simulink (R) Interface
Hybrid Systems: Continuous- and Discrete-Time Components
Monte Carlo Simulation
Case Study: Pilot Ejection
Case Study: Kalman Filtering
Intermediate Numerical Integration
Runge-Kutta (RK)-One-Step Methods
Adaptive Techniques
Multistep Methods
Stiff Systems
Lumped Parameter Approximation of Distributed Parameter Systems
Systems with Discontinuities
Case Study: Spread of an Epidemic
Simulation Tools
Steady-State Solver
Optimization of Simulink (R) Models
Linearization
Adding Blocks to the Simulink (R) Library Browser
Simulation Acceleration
Advanced Numerical Integration
Dynamic Errors (Characteristic Roots, Transfer Function)
Stability of Numerical Integrators
Multirate Integration
Real-Time Simulation
Additional Methods of Approximating Continuous-Time System Models
Case Study: Lego Mindstorms NXT
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