Applied Control Systems 1: autonomous cars: Math + PID + MPC
About Course
Free Applied Control Systems Course: Learn Autonomous Car Technology
This comprehensive Applied Control Systems course will teach you the fundamentals of controlling autonomous systems. Learn how to design, master, and apply mathematical models, PID controllers, and Model Predictive Controllers (MPC) to create selfdriving cars and other autonomous vehicles. This course will help you develop the intuition, mathematics, and coding skills you need to succeed in the field of control systems engineering.
You’ll learn:
 How to create mathematical models for statespace systems and equations of motion
 How to design a PID controller for a magnetic train that needs to catch objects falling from the sky
 How to use MPC to create an autonomous car that can change lanes on a straight road at a constant forward speed
This course covers topics like:
 Autonomous vehicles
 Control systems engineering
 PID controllers
 MPC
 Statespace systems
 Equations of motion
 Mathematical modeling
 Coding
Enroll today and start learning how to build the future of autonomous systems! This course is completely free and available on Theetay. We offer a wide range of courses from platforms like Udemy, Udacity, Coursera, MasterClass, NearPeer, and more.
Course Content
Intro to Control – PID controller

Course guide
02:50 
Intro to Control – how to control systems with a controller 1
06:52 
Intro to Control – how to control systems with a controller 2
06:34 
Open VS Closed Loop System
06:36 
Controlling the water tank in a Python simulation
02:52 
Intro to a proportional controller
04:44 
Modelling the water tank 1
01:45 
Modelling the water tank 2
12:13 
Numerical integration applied to the water tank model
09:52 
Combining math with the control structure
07:07 
Water tank simulation – proportional controller
02:28 
Intro to a PID simulation
02:26 
Follow up!
00:58 
PID Modelling the train with forces 1
06:27 
PID Modelling the train with forces 2
09:36 
PID Going from system input to system output using numerical integration
10:00 
PID Magnetic train simulation – proportional controller
01:59 
PID Proportional controller overshoot explanation 1
04:39 
PID Proportional controller overshoot explanation 2
06:28 
PID Proportional controller overshoot explanation 3
03:40 
PID Intro to Derivative Control
10:24 
PID Tuning the controller
06:11 
PID Proportional & Derivative controller & magnetic train simulation in Python
09:01 
PID Intro to Integral Control
04:35 
PID Python magnetic train simulation at an inclination angle
01:49 
PID Mathematical modelling of the train with the inclination angle 1
03:43 
PID Mathematical modelling of the train with the inclination angle 2
05:39 
PID Proporti
15:15 
PID Magnetic train simulation (inclination angle & PID)
02:26 
Intro to (Linux & macOS Terminal) & (Windows Command Prompt)
12:54 
Installing the Python environment and its libraries (Linux Ubuntu)
06:45 
Installing the Python environment and its libraries (Windows 10)
06:34 
Installing the Python environment and its libraries (macOS)
08:13 
PID train code explanation 1
17:56 
PID train code explanation 2
11:15 
PID train code explanation 3
11:18 
Short intro to Python animation tools
12:24 
Quick code & animation explanation (water tanks)
28:29 
Course Material Download Link
00:00
Fundamentals of forces, moments, mass moment of inertia and reference frames
Vehicle modelling for lateral control using equations of motion
Vehicle’s statespace & Linear Time Invariant (LTI) model for lateral control
Model Predictive Control – Intuition – Rocket example
Model Predictive Control – Mathematical Derivation – autonomous vehicle example
Model Predictive Control – Python Simulation – autonomous vehicle
9
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