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

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