Dynamic System Modeling and Analysis with MATLAB and Python: For Control Engineers

Description

Dynamic System Modeling & Analysis with MATLAB & Python A robust introduction to the advanced programming techniques and skills needed for control engineering In Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers, accomplished control engineer Dr. Jongrae Kim delivers an insightful and concise introduction to the advanced programming skills required by control engineers. The book discusses dynamic systems used by satellites, aircraft, autonomous robots, and biomolecular networks. Throughout the text, MATLAB and Python are used to consider various dynamic modeling theories and examples. The author covers a range of control topics, including attitude dynamics, attitude kinematics, autonomous vehicles, systems biology, optimal estimation, robustness analysis, and stochastic system. An accompanying website includes a solutions manual as well as MATLAB and Python example code. Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers provides readers with a sound starting point to learning programming in the engineering or biology domains. It also offers: A thorough introduction to attitude estimation and control, including attitude kinematics and sensors and extended Kalman filters for attitude estimationPractical discussions of autonomous vehicles mission planning, including unmanned aerial vehicle path planning and moving target trackingComprehensive explorations of biological network modeling, including bio-molecular networks and stochastic modelingIn-depth examinations of control algorithms using biomolecular networks, including implementation Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers is an indispensable resource for advanced undergraduate and graduate students seeking practical programming instruction for dynamic system modeling and analysis using control theory. Cover Title Page Copyright Contents Preface Acknowledgements Acronyms About the Companion Website Chapter 1 Introduction 1.1 Scope of the Book 1.2 Motivation Examples 1.2.1 Free‐Falling Object 1.2.1.1 First Program in Matlab 1.2.1.2 First Program in Python 1.2.2 Ligand–Receptor Interactions 1.3 Organization of the Book Exercises Bibliography Chapter 2 Attitude Estimation and Control 2.1 Attitude Kinematics and Sensors 2.1.1 Solve Quaternion Kinematics 2.1.1.1 MATLAB 2.1.1.2 Python 2.1.2 Gyroscope Sensor Model 2.1.2.1 Zero‐Mean Gaussian White Noise 2.1.2.2 Generate Random Numbers 2.1.2.3 Stochastic Process 2.1.2.4 MATLAB 2.1.2.5 Python 2.1.2.6 Gyroscope White Noise 2.1.2.7 Gyroscope Random Walk Noise 2.1.2.8 Gyroscope Simulation 2.1.3 Optical Sensor Model 2.2 Attitude Estimation Algorithm 2.2.1 A Simple Algorithm 2.2.2 QUEST Algorithm 2.2.3 Kalman Filter 2.2.4 Extended Kalman Filter 2.2.4.1 Error Dynamics 2.2.4.2 Bias Noise 2.2.4.3 Noise Propagation in Error Dynamics 2.2.4.4 State Transition Matrix, Φ 2.2.4.5 Vector Measurements 2.2.4.6 Summary 2.2.4.7 Kalman Filter Update 2.2.4.8 Kalman Filter Propagation 2.3 Attitude Dynamics and Control 2.3.1 Dynamics Equation of Motion 2.3.1.1 MATLAB 2.3.1.2 Python 2.3.2 Actuator and Control Algorithm 2.3.2.1 MATLAB Program 2.3.2.2 Python 2.3.2.3 Attitude Control Algorithm 2.3.2.4 Altitude Control Algorithm 2.3.2.5 Simulation 2.3.2.6 MATLAB 2.3.2.7 Robustness Analysis 2.3.2.8 Parallel Processing Exercises Bibliography Chapter 3 Autonomous Vehicle Mission Planning 3.1 Path Planning 3.1.1 Potential Field Method 3.1.1.1 MATLAB 3.1.1.2 Python 3.1.2 Graph Theory‐Based Sampling Method 3.1.2.1 MATLAB 3.1.2.2 Python 3.1.2.3 Dijkstra's Shortest Path Algorithm 3.1.2.4 MATLAB 3.1.2.5 Python 3.1.3 Complex Obstacles 3.1.3.1 MATLAB 3.1.3.2 Python 3.2 Moving Target Tracking 3.2.1 UAV and Moving Target Model 3.2.2 Optimal Target Tracking Problem 3.2.2.1 MATLAB 3.2.2.2 Python 3.2.2.3 Worst‐Case Scenario 3.2.2.4 MATLAB 3.2.2.5 Python 3.2.2.6 Optimal Control Input 3.3 Tracking Algorithm Implementation 3.3.1 Constraints 3.3.1.1 Minimum Turn Radius Constraints 3.3.1.2 Velocity Constraints 3.3.2 Optimal Solution 3.3.2.1 Control Input Sampling 3.3.2.2 Inside the Constraints 3.3.2.3 Optimal Input 3.3.3 Verification Simulation Exercises Bibliography Chapter 4 Biological System Modelling 4.1 Biomolecular Interactions 4.2 Deterministic Modelling 4.2.1 Group of Cells and Multiple Experiments 4.2.1.1 Model Fitting and the Measurements 4.2.1.2 Finding Adaptive Parameters 4.2.2 E. coli Tryptophan Regulation Model 4.2.2.1 Steady‐State and Dependant Parameters 4.2.2.2 Padé Approximation of Time‐Delay 4.2.2.3 State‐Space Realization 4.2.2.4 Python 4.2.2.5 Model Parameter Ranges 4.2.2.6 Model Fitting Optimization 4.2.2.7 Optimal Solution (MATLAB) 4.2.2.8 Optimal Solution (Python) 4.2.2.9 Adaptive Parameters 4.2.2.10 Limitations 4.3 Biological Oscillation 4.3.1 Gillespie's Direct Method 4.3.2 Simulation Implementation 4.3.3 Robustness Analysis Exercises Bibliography Chapter 5 Biological System Control 5.1 Control Algorithm Implementation 5.1.1 PI Controller 5.1.1.1 Integral Term 5.1.1.2 Proportional Term 5.1.1.3 Summation of the Proportional and the Integral Terms 5.1.1.4 Approximated PI Controller 5.1.1.5 Comparison of PI Controller and the Approximation 5.1.2 Error Calculation: ΔP 5.2 Robustness Analysis: μ‐Analysis 5.2.1 Simple Examples 5.2.1.1 μ Upper Bound 5.2.1.2 μ Lower Bound 5.2.1.3 Complex Numbers in MATLAB/Python 5.2.2 Synthetic Circuits 5.2.2.1 MATLAB 5.2.2.2 Python 5.2.2.3 μ‐Upper Bound: Geometric Approach Exercises Bibliography Chapter 6 Further Readings 6.1 Boolean Network 6.2 Network Structure Analysis 6.3 Spatial‐Temporal Dynamics 6.4 Deep Learning Neural Network 6.5 Reinforcement Learning Bibliography Appendix A Solutions for Selected Exercises A.1 Chapter 1 Exercise 1.4 Exercise 1.5 A.2 Chapter 2 Exercise 2.5 A.3 Chapter 3 Exercise 3.1 Exercise 3.6 A.4 Chapter 4 Exercise 4.1 Exercise 4.2 Exercise 4.7 A.5 Chapter 5 Exercise 5.2 Exercise 5.3 Index EULA