Modeling and Simulation of Systems Using MATLAB and Simulink / Edition 1

Modeling and Simulation of Systems Using MATLAB and Simulink / Edition 1

by Devendra K. Chaturvedi
ISBN-10:
1439806721
ISBN-13:
9781439806722
Pub. Date:
12/16/2009
Publisher:
Taylor & Francis

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Overview

Modeling and Simulation of Systems Using MATLAB and Simulink / Edition 1

Not only do modeling and simulation help provide a better understanding of how real-world systems function, they also enable us to predict system behavior before a system is actually built and analyze systems accurately under varying operating conditions. Modeling and Simulation of Systems Using MATLAB® and Simulink® provides comprehensive, state-of-the-art coverage of all the important aspects of modeling and simulating both physical and conceptual systems. Various real-life examples show how simulation plays a key role in understanding real-world systems. The author also explains how to effectively use MATLAB and Simulink software to successfully apply the modeling and simulation techniques presented.

After introducing the underlying philosophy of systems, the book offers step-by-step procedures for modeling different types of systems using modeling techniques, such as the graph-theoretic approach, interpretive structural modeling, and system dynamics modeling. It then explores how simulation evolved from pre-computer days into the current science of today. The text also presents modern soft computing techniques, including artificial neural networks, fuzzy systems, and genetic algorithms, for modeling and simulating complex and nonlinear systems. The final chapter addresses discrete systems modeling.

Preparing both undergraduate and graduate students for advanced modeling and simulation courses, this text helps them carry out effective simulation studies. In addition, graduate students should be able to comprehend and conduct simulation research after completing this book.


    Product Details

    ISBN-13: 9781439806722
    Publisher: Taylor & Francis
    Publication date: 12/16/2009
    Pages: 734
    Product dimensions: 7.10(w) x 10.10(h) x 2.00(d)

    About the Author

    Devendra K. Chaturvedi is a professor in the Department of Electrical Engineering at Dayalbagh Educational Institute in India.

    Table of Contents

    Introduction to Systems

    System

    Classification of Systems

    Linear Systems

    Time-Varying vs. Time-Invariant Systems

    Lumped vs. Distributed Parameter Systems

    Continuous- and Discrete-Time Systems

    Deterministic vs. Stochastic Systems

    Hard and Soft Systems

    Analysis of Systems

    Synthesis of Systems

    Introduction to System Philosophy

    System Thinking

    Large and Complex Applied System Engineering: A Generic Modeling

    Systems Modeling

    Introduction

    Need of System Modeling

    Modeling Methods for Complex Systems

    Classification of Models

    Characteristics of Models

    Modeling

    Mathematical Modeling of Physical Systems

    Formulation of State Space Model of Systems

    Physical Systems Theory

    System Components and Interconnections

    Computation of Parameters of a Component

    Single Port and Multiport Systems

    Techniques of System Analysis

    Basics of Linear Graph Theoretic Approach

    Formulation of System Model for Conceptual System

    Formulation System Model for Physical Systems

    Topological Restrictions

    Development of State Model of Degenerative System

    Solution of State Equations

    Controllability

    Observability

    Sensitivity

    Liapunov Stability

    Performance Characteristics of Linear Time Invariant Systems

    Formulation of State Space Model Using Computer Program (SYSMO)

    Model Order Reduction

    Introduction

    Difference between Model Simplification and Model Order Reduction

    Need for Model Order Reduction

    Principle of Model Order Reduction

    Methods of Model Order Reduction

    Applications of Reduced-Order Models

    Analogous of Linear Systems

    Introduction

    Force–Voltage (f–v) Analogy

    Force–Current (f–i) Analogy

    Interpretive Structural Modeling

    Introduction

    Graph Theory

    Interpretive Structural Modeling

    System Dynamics Techniques

    Introduction

    System Dynamics of Managerial and Socioeconomic System

    Traditional Management

    Sources of Information

    Strength of System Dynamics

    Experimental Approach to System Analysis

    System Dynamics Technique

    Structure of a System Dynamic Model

    Basic Structure of System Dynamics Models

    Different Types of Equations Used in System Dynamics Techniques

    Symbol Used in Flow Diagrams

    Dynamo Equations

    Modeling and Simulation of Parachute Deceleration Device

    Modeling of Heat Generated in a Parachute during Deployment

    Modeling of Stanchion System of Aircraft Arrester Barrier System

    Simulation

    Introduction

    Advantages of Simulation

    When to Use Simulations

    Simulation Provides

    How Simulations Improve Analysis and Decision Making

    Applications of Simulation

    Numerical Methods for Simulation

    The Characteristics of Numerical Methods

    Comparison of Different Numerical Methods

    Errors during Simulation with Numerical Methods

    Nonlinear and Chaotic Systems

    Introduction

    Linear vs. Nonlinear System

    Types of Nonlinearities

    Nonlinearities in Flight Control of Aircraft

    Conclusions

    Introduction to Chaotic System

    Historical Prospective

    First-Order Continuous-Time System

    Bifurcations

    Second-Order System

    Third-Order System

    Modeling with Artificial Neural Network

    Introduction

    Artificial Neural Networks

    Modeling Using Fuzzy Systems

    Introduction

    Fuzzy Sets

    Features of Fuzzy Sets

    Operations on Fuzzy Sets

    Characteristics of Fuzzy Sets

    Properties of Fuzzy Sets

    Fuzzy Cartesian Product

    Fuzzy Relation

    Approximate Reasoning

    Defuzzification Methods

    Introduction to Fuzzy Rule–Based Systems

    Applications of Fuzzy Systems to System Modeling

    Takagi–Sugeno–Kang Fuzzy Models

    Adaptive Neuro-Fuzzy Inferencing Systems

    Steady State DC Machine Model

    Transient Model of a DC Machine

    Fuzzy System Applications for Operations Research

    Discrete-Event Modeling and Simulation

    Introduction

    Some Important Definitions

    Queuing System

    Discrete-Event System Simulation

    Components of Discrete-Event System Simulation

    Input Data Modeling

    Family of Distributions for Input Data

    Random Number Generation

    Chi-Square Test

    Kolomogrov–Smirnov Test

    Appendix A: MATLAB

    Appendix B: Simulink

    Appendix C: Glossary

    Index

    Customer Reviews

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