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Dynamic System Modelling and Analysis with MATLAB and Python: For Control Engineers [Kõva köide]

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(University of Leeds, Leeds, UK)
Teised raamatud teemal:
Dynamic System Modelling and Analysis with MATLAB and Python: For Control EngineersSuurem pilt
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Püsilink: https://www.kriso.ee/db/9781119801627.html
Märksõnad:
"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

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 estimation
  • Practical discussions of autonomous vehicles mission planning, including unmanned aerial vehicle path planning and moving target tracking
  • Comprehensive explorations of biological network modeling, including bio-molecular networks and stochastic modeling
  • In-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.

Preface xiii
Acknowledgements xv
Acronyms xvii
About the Companion Website xix
1 Introduction
1(22)
1.1 Scope of the Book
1(1)
1.2 Motivation Examples
2(19)
1.2.1 Free-Falling Object
2(2)
1.2.1.1 First Program in Matlab
4(6)
1.2.1.2 First Program in Python
10(4)
1.2.2 Ligand-Receptor Interactions
14(7)
1.3 Organization of the Book
21(2)
Exercises
21(1)
Bibliography
22(1)
2 Attitude Estimation and Control
23(96)
2.1 Attitude Kinematics and Sensors
23(41)
2.1.1 Solve Quaternion Kinematics
26(1)
2.1.1.1 MATLAB
26(3)
2.1.1.2 Python
29(4)
2.1.2 Gyroscope Sensor Model
33(1)
2.1.2.1 Zero-Mean Gaussian White Noise
33(1)
2.1.2.2 Generate Random Numbers
34(6)
2.1.2.3 Stochastic Process
40(1)
2.1.2.4 MATLAB
41(4)
2.1.2.5 Python
45(4)
2.1.2.6 Gyroscope White Noise
49(1)
2.1.2.7 Gyroscope Random Walk Noise
50(3)
2.1.2.8 Gyroscope Simulation
53(4)
2.1.3 Optical Sensor Model
57(7)
2.2 Attitude Estimation Algorithm
64(24)
2.2.1 A Simple Algorithm
64(1)
2.2.2 QUEST Algorithm
65(1)
2.2.3 Kalman Filter
66(9)
2.2.4 Extended Kalman Filter
75(1)
2.2.4.1 Error Dynamics
76(1)
2.2.4.2 Bias Noise
77(1)
2.2.4.3 Noise Propagation in Error Dynamics
78(6)
2.2.4.4 State Transition Matrix, Φ
84(1)
2.2.4.5 Vector Measurements
84(2)
2.2.4.6 Summary
86(1)
2.2.4.7 Kalman Filter Update
86(1)
2.2.4.8 Kalman Filter Propagation
87(1)
2.3 Attitude Dynamics and Control
88(31)
2.3.1 Dynamics Equation of Motion
88(3)
2.3.1.1 MATLAB
91(3)
2.3.1.2 Python
94(1)
2.3.2 Actuator and Control Algorithm
95(3)
2.3.2.1 MATLAB Program
98(3)
2.3.2.2 Python
101(2)
2.3.2.3 Attitude Control Algorithm
103(2)
2.3.2.4 Altitude Control Algorithm
105(1)
2.3.2.5 Simulation
106(1)
2.3.2.6 MATLAB
107(1)
2.3.2.7 Robustness Analysis
107(3)
2.3.2.8 Parallel Processing
110(3)
Exercises
113(2)
Bibliography
115(4)
3 Autonomous Vehicle Mission Planning
119(66)
3.1 Path Planning
119(26)
3.1.1 Potential Field Method
119(3)
3.1.1.1 MATLAB
122(4)
3.1.1.2 Python
126(1)
3.1.2 Graph Theory-Based Sampling Method
126(2)
3.1.2.1 MATLAB
128(1)
3.1.2.2 Python
129(1)
3.1.2.3 Dijkstra's Shortest Path Algorithm
130(1)
3.1.2.4 MATLAB
130(1)
3.1.2.5 Python
131(3)
3.1.3 Complex Obstacles
134(1)
3.1.3.1 MATLAB
135(6)
3.1.3.2 Python
141(4)
3.2 Moving Target Tracking
145(22)
3.2.1 UAV and Moving Target Model
145(3)
3.2.2 Optimal Target Tracking Problem
148(1)
3.2.2.1 MATLAB
149(2)
3.2.2.2 Python
151(2)
3.2.2.3 Worst-Case Scenario
153(4)
3.2.2.4 MATLAB
157(2)
3.2.2.5 Python
159(5)
3.2.2.6 Optimal Control Input
164(3)
3.3 Tracking Algorithm Implementation
167(18)
3.3.1 Constraints
167(1)
3.3.1.1 Minimum Turn Radius Constraints
167(2)
3.3.1.2 Velocity Constraints
169(3)
3.3.2 Optimal Solution
172(1)
3.3.2.1 Control Input Sampling
172(3)
3.3.2.2 Inside the Constraints
175(2)
3.3.2.3 Optimal Input
177(3)
3.3.3 Verification Simulation
180(2)
Exercises
182(1)
Bibliography
182(3)
4 Biological System Modelling
185(66)
4.1 Biomolecular Interactions
185(1)
4.2 Deterministic Modelling
185(42)
4.2.1 Group of Cells and Multiple Experiments
186(2)
4.2.1.1 Model Fitting and the Measurements
188(2)
4.2.1.2 Finding Adaptive Parameters
190(1)
4.2.2 E. coli Tryptophan Regulation Model
191(3)
4.2.2.1 Steady-State and Dependant Parameters
194(1)
4.2.2.2 Pade Approximation of Time-Delay
195(1)
4.2.2.3 State-Space Realization
196(9)
4.2.2.4 Python
205(1)
4.2.2.5 Model Parameter Ranges
206(7)
4.2.2.6 Model Fitting Optimization
213(8)
4.2.2.7 Optimal Solution (MATLAB)
221(2)
4.2.2.8 Optimal Solution (Python)
223(3)
4.2.2.9 Adaptive Parameters
226(1)
4.2.2.10 Limitations
226(1)
4.3 Biological Oscillation
227(24)
4.3.1 Gillespie's Direct Method
231(3)
4.3.2 Simulation Implementation
234(7)
4.3.3 Robustness Analysis
241(4)
Exercises
245(1)
Bibliography
246(5)
5 Biological System Control
251(44)
5.1 Control Algorithm Implementation
251(18)
5.1.1 PI Controller
251(1)
5.1.1.1 Integral Term
252(1)
5.1.1.2 Proportional Term
253(1)
5.1.1.3 Summation of the Proportional and the Integral Terms
253(1)
5.1.1.4 Approximated PI Controller
253(1)
5.1.1.5 Comparison of PI Controller and the Approximation
254(6)
5.1.2 Error Calculation: AP
260(9)
5.2 Robustness Analysis: μ-Analysis
269(26)
5.2.1 Simple Examples
269(3)
5.2.1.1 μ Upper Bound
272(3)
5.2.1.2 μ Lower Bound
275(3)
5.2.1.3 Complex Numbers in MATLAB/Python
278(2)
5.2.2 Synthetic Circuits
280(1)
5.2.2.1 MATLAB
281(1)
5.2.2.2 Python
281(9)
5.2.2.3 μ-Upper Bound: Geometric Approach
290(1)
Exercises
291(1)
Bibliography
292(3)
6 Further Readings
295(6)
6.1 Boolean Network
295(1)
6.2 Network Structure Analysis
296(1)
6.3 Spatial-Temporal Dynamics
297(1)
6.4 Deep Learning Neural Network
298(1)
6.5 Reinforcement Learning
298(3)
Bibliography
298(3)
Appendix A Solutions for Selected Exercises
301(6)
A.1
Chapter 1
301(1)
Exercise 1.4
301(1)
Exercise 1.5
301(1)
A.2
Chapter 2
302(1)
Exercise 2.5
302(1)
A.3
Chapter 3
302(1)
Exercise 3.1
302(1)
Exercise 3.6
303(1)
A.4
Chapter 4
303(1)
Exercise 4.1
303(1)
Exercise 4.2
303(1)
Exercise 4.7
304(1)
A. 5
Chapter 5
304(3)
Exercise 5.2
304(1)
Exercise 5.3
304(3)
Index 307
Jongrae Kim, PhD, is Associate Professor at the Institute of Design, Robotics & Optimization (iDRO), School of Mechanical Engineering, University of Leeds in the United Kingdom. He obtained his doctorate from the Department of Aerospace Engineering at Texas A&M University in the United States in 2002.