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<p>Preface xiii</p> <p>About the Companion Website xvii</p> <p><b>1 Introduction 1</b></p> <p>1.1 Introduction 1</p> <p>1.1.1 Systems Engineering 1</p> <p>1.1.2 The Input/Output Viewpoint 2</p> <p>1.1.3 Some Examples 2</p> <p>1.2 Model Classification 5</p> <p>1.2.1 Static and Dynamic Systems 5</p> <p>1.2.2 Linear and Nonlinear Systems 5</p> <p>1.2.3 Distributed-Parameter Systems 6</p> <p>1.2.4 Hybrid and Discrete-Event Systems 6</p> <p>1.2.5 Deterministic and Stochastic Systems 7</p> <p>1.2.6 Large-Scale Systems 7</p> <p>1.3 Simulation Languages 9</p> <p>1.4 Outline of the Text 10</p> <p>Problems 11</p> <p><b>2 Second-Order Systems 15</b></p> <p>2.1 Introduction 15</p> <p>2.2 State-Space Representation 19</p> <p>2.3 Trajectories and Phase Portraits 22</p> <p>2.4 The Direction Field 27</p> <p>2.5 Equilibria 30</p> <p>2.6 Linear Systems 33</p> <p>2.7 Linearization of Nonlinear Systems 41</p> <p>2.8 Periodic Trajectories and Limit Cycles 45</p> <p>2.8.1 Relaxation Oscillators 45</p> <p>2.8.2 Bendixson’s Theorem 49</p> <p>2.8.3 Poincaré–Bendixson Theorem 51</p> <p>2.9 Coupled Second-Order Systems 53</p> <p>Problems 55</p> <p><b>3 System Fundamentals 61</b></p> <p>3.1 Introduction 61</p> <p>3.2 Existence and Uniqueness of Solution 61</p> <p>3.3 The Matrix Exponential 64</p> <p>3.4 The Jordan Canonical Form 67</p> <p>3.5 Linearization 71</p> <p>3.6 The Hartman–Grobman Theorem 72</p> <p>3.7 Singular Perturbations 73</p> <p>Problems 79</p> <p><b>4 Compartmental Models 83</b></p> <p>4.1 Introduction 83</p> <p>4.2 Exponential Growth and Decay 84</p> <p>4.3 The Logistic Equation 87</p> <p>4.4 Models of Epidemics 88</p> <p>4.5 Predator–Prey System 95</p> <p>Problems 97</p> <p><b>5 Stability 101</b></p> <p>5.1 Introduction 101</p> <p>5.2 Lyapunov Stability 102</p> <p>5.3 Basin of Attraction 109</p> <p>5.4 The Invariance Principle 110</p> <p>5.5 Linear Systems and Linearization 113</p> <p>Problems 116</p> <p><b>6 Discrete-Time Systems 119</b></p> <p>6.1 Introduction 119</p> <p>6.2 Stability of Discrete-Time Systems 123</p> <p>6.3 Stability of Discrete-Time Linear Systems 124</p> <p>6.4 Moving-Average Filter 126</p> <p>6.5 Cobweb Diagrams 128</p> <p>6.5.1 Cobweb Diagrams in Economics 130</p> <p>6.5.2 The Discrete Logistic Equation 131</p> <p>Problems 134</p> <p><b>7 Numerical Methods 137</b></p> <p>7.1 Introduction 137</p> <p>7.2 Numerical Differentiation 138</p> <p>7.3 Numerical Integration 141</p> <p>7.4 Numerical Solution of ODEs 147</p> <p>7.4.1 Euler Predictor–Corrector Method 150</p> <p>7.4.2 Runge–Kutta Methods 152</p> <p>7.5 Stiff Systems 155</p> <p>7.6 Event Detection 160</p> <p>7.7 Simulink 163</p> <p>7.8 Summary 168</p> <p>Problems 169</p> <p><b>8 Optimization 173</b></p> <p>8.1 Introduction 173</p> <p>8.2 Unconstrained Optimization 177</p> <p>8.2.1 Iterative Search 179</p> <p>8.2.2 Gradient Descent 180</p> <p>8.2.3 Newton’s Method 184</p> <p>8.3 Case Study: Numerical Inverse Kinematics 187</p> <p>8.4 Constrained Optimization 191</p> <p>8.4.1 Equality Constraints 191</p> <p>8.4.2 Inequality Constraints 196</p> <p>8.5 Convex Optimization 200</p> <p>Problems 204</p> <p><b>9 System Identification 209</b></p> <p>9.1 Introduction 209</p> <p>9.2 Least Squares 209</p> <p>9.3 Regression 212</p> <p>9.4 Recursive Least Squares 217</p> <p>9.5 Logistic Regression 220</p> <p>9.6 Neural Networks 224</p> <p>Problems 230</p> <p><b>10 Stochastic Systems 233</b></p> <p>10.1 Markov Chains 233</p> <p>10.1.1 Regular and Ergodic Markov Chains 240</p> <p>10.1.2 Absorbing Markov Chains 244</p> <p>10.2 Monte Carlo Methods 249</p> <p>10.2.1 Random Number Generation 250</p> <p>10.2.2 Monte Carlo Integration 253</p> <p>10.2.3 Monte Carlo Optimization 255</p> <p>10.2.4 Monte Carlo Simulation 255</p> <p>Problems 258</p> <p><b>11 Feedback Systems 261</b></p> <p>11.1 Introduction 261</p> <p>11.2 Transfer Functions 263</p> <p>11.3 Feedback Control 269</p> <p>11.4 State-Space Models 273</p> <p>11.4.1 Minimal Realizations 274</p> <p>11.4.2 Pole Placement 280</p> <p>11.4.3 State Estimation 283</p> <p>11.4.4 The Separation Principle 285</p> <p>11.5 Optimal Control 288</p> <p>11.6 Control of Nonlinear Systems 289</p> <p>Problems 292</p> <p><b>12 Partial Differential Equation Models 297</b></p> <p>12.1 Introduction 297</p> <p>12.1.1 Existence and Uniqueness of Solutions 297</p> <p>12.1.2 Classification of Linear Second-Order PDEs 298</p> <p>12.2 The Wave Equation 299</p> <p>12.2.1 The D’Alembert Solution 300</p> <p>12.2.2 Initial-Value Problem 300</p> <p>12.2.3 Separation of Variables 302</p> <p>12.3 The Heat Equation 310</p> <p>12.4 Laplace’s Equation 313</p> <p>12.5 Numerical Solution of PDEs 315</p> <p>Problems 319</p> <p><b>13 Complex Networks 321</b></p> <p>13.1 Introduction 321</p> <p>13.1.1 Examples of Complex Networks 322</p> <p>13.2 Graph Theory: Basic Concepts 324</p> <p>13.2.1 Graph Isomorphism 327</p> <p>13.2.2 Connectivity 327</p> <p>13.2.3 Trees 331</p> <p>13.2.4 Bipartite Graphs 332</p> <p>13.2.5 Planar Graphs 333</p> <p>13.2.6 Graphs and Matrices 335</p> <p>13.3 Matlab Graph Functions 341</p> <p>13.4 Network Metrics 343</p> <p>13.4.1 Degree Distribution 343</p> <p>13.4.2 Centrality 347</p> <p>13.4.3 Clustering 350</p> <p>13.5 Random Graphs 354</p> <p>13.5.1 Erdős–Rényi Networks 354</p> <p>13.5.2 Small-World Networks 358</p> <p>13.5.3 Scale-Free Networks 360</p> <p>13.6 Synchronization in Networks 362</p> <p>Problems 366</p> <p><b>Appendix A Linear Algebra 371</b></p> <p>A. 1 Vectors 371</p> <p>A. 2 Matrices 373</p> <p>A. 3 Eigenvalues and Eigenvectors 375</p> <p><b>Appendix B Real Analysis 379</b></p> <p>B. 1 Set Theory 379</p> <p>B. 2 Vector Fields 380</p> <p>B. 3 Jacobian 381</p> <p>B. 4 Scalar Functions 381</p> <p>B. 5 Taylor’s Theorem 382</p> <p>B. 6 Extreme-Value Theorem 383</p> <p><b>Appendix C Probability 385</b></p> <p>C.1 Discrete Probability 385</p> <p>C.2 Conditional Probability 386</p> <p>C.3 Random Variables 389</p> <p>C.4 Continuous Probability 391</p> <p><b>Appendix D Proofs of Selected Results 395</b></p> <p>D. 1 Proof of Theorem 2.2 395</p> <p>D. 2 Proof of Theorem 5.1 395</p> <p>D. 3 Proof of Theorem 5.5 396</p> <p>D. 4 Proof of Theorem 13.3 397</p> <p>D. 5 Proof of Corollary 13.2 397</p> <p>D. 6 Proof of Proposition 13.2 398</p> <p>D. 7 Proof of Proposition 13.3 398</p> <p><b>Appendix E Matlab Command Reference 399</b></p> <p>References 403</p> <p>Index 407</p>

<p><b>Mark W. Spong, DSc,</b> is Professor of Systems Engineering and Professor of Electrical and Computer Engineering at the University of Texas, USA, where he also holds the Excellence in Education Chair. Professor Spong received his doctorate in systems science and mathematics in 1981 from Washington University in St. Louis, USA. He is a Life Fellow of the IEEE and a Fellow of IFAC. Among the numerous notable awards he has received are the 2011 Pioneer in Robotics Award from the IEEE Robotics and Automation Society, the 2020 Rufus Oldenburger Medal from the ASME, the 2018 Bode Lecture Prize from the IEEE Control Systems Society, the 2016 Nyquist Lecture Prize from the ASME, and the IEEE Transactions on Control Systems Technology Outstanding Paper Award.

<p><b>An essential introduction to engineering system modeling and simulation from a well-trusted source in engineering and education</b> <p>This new introductory-level textbook provides thirteen self-contained chapters, each covering an important topic in engineering systems modeling and simulation. The importance of such a topic cannot be overstated; modeling and simulation will only increase in importance in the future as computational resources improve and become more powerful and accessible, and as systems become more complex. This resource is a wonderful mix of practical examples, theoretical concepts, and experimental sessions that ensure a well-rounded education on the topic. <p>The topics covered in <i>Introduction to Modeling and Simulation</i> are timeless fundamentals that provide the necessary background for further and more advanced study of one or more of the topics. The text includes topics such as linear and nonlinear dynamical systems, continuous-time and discrete-time systems, stability theory, numerical methods for solution of ODEs, PDE models, feedback systems, optimization, regression and more. Each chapter provides an introduction to the topic to familiarize students with the core ideas before delving deeper. The numerous tools and examples help ensure students engage in active learning, acquiring a range of tools for analyzing systems and gaining experience in numerical computation and simulation systems, from an author prized for both his writing and his teaching over the course of his over-40-year career. <p><i>Introduction to Modeling and Simulation</i> readers will also find: <ul><li> Numerous examples, tools, and programming tips to help clarify points made throughout the textbook, with end-of-chapter problems to further emphasize the material</li> <li> As systems become more complex, a chapter devoted to complex networks including small-world and scale-free networks – a unique advancement for textbooks within modeling and simulation</li> <li> A complementary website that hosts a complete set of lecture slides, a solution manual for end-of-chapter problems, MATLAB files, and case-study exercises</li></ul> <p><i>Introduction to Modeling and Simulation</i> is aimed at undergraduate and first-year graduate engineering students studying systems, in diverse avenues within the field: electrical, mechanical, mathematics, aerospace, bioengineering, physics, and civil and environmental engineering. It may also be of interest to those in mathematical modeling courses, as it provides in-depth material on MATLAB simulation and contains appendices with brief reviews of linear algebra, real analysis, and probability theory.

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