1st Edition
Modeling and Simulation of Systems Using MATLAB and Simulink
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.
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
Biography
Devendra K. Chaturvedi is a professor in the Department of Electrical Engineering at Dayalbagh Educational Institute in India.
