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 self-driving 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 state-space 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
State-space 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

PID VS Model Predictive Control (MPC) 1

02:42
Intro to MPC

01:10
Getting started with modelling a car

05:06
Fundamentals of forces and moments 1

12:10
Fundamentals of forces and moments 2

09:58
Setting stage for the car’s lateral control 1

05:09
Setting stage for the car’s lateral control 2

08:50
PID VS Model Predictive Control (MPC) 2

01:15
PID VS Model Predictive Control (MPC) 2

01:15
Setting stage for the car’s lateral control 3

08:25
Setting stage for the car’s lateral control 4

01:27

Vehicle modelling for lateral control using equations of motion

Follow up!

00:58
The general control structure for the vehicle’s lateral control

02:31
Car model VS simplified bicycle model 1

04:59
Car model VS simplified bicycle model 2

01:41
Car model VS simplified bicycle model 3

02:52
Ackerman Steering

01:52
Longitudinal & lateral velocities of the bicycle model 1

04:37
Longitudinal & lateral velocities of the bicycle model 2

03:32
Equations of motion in the lateral direction

03:15
Lateral & centripetal acceleration

05:28
Centripetal acceleration intuition & mathematical derivation 1

05:39
Centripetal acceleration intuition & mathematical derivation 2

08:58
Centripetal acceleration intuition & mathematical derivation 3

20:51
Modelling the front wheel of the vehicle 1

03:41
Rewriting lateral forces in terms of front wheel angles

03:39
Modelling the front wheel of the vehicle 2

02:09
Modelling the front wheel of the vehicle 3

07:26
Modelling the front wheel of the vehicle 4

08:10

Vehicle’s state-space & Linear Time Invariant (LTI) model for lateral control

From equations of motion to state-space equations 1

01:36
From equations of motion to state-space equations 2

08:09
From equations of motion to state-space equations 3

05:58
From equations of motion to state-space equations 4

03:39
The meaning of states 1

05:21
The meaning of states 2

04:59
Adding extra states to the system

09:15
Computing new states in the open loop system 1

12:16
Computing new states in the open loop system 2

09:58
Computing new states in the open loop system 3

05:46
Simplifying systems with small angle assumptions

08:54
Nonlinear VS Linear Time Invariant (LTI) models

11:47
Connecting LTI matrices with the vehicle’s inputs

06:30
Getting LTI model using small angle approximation 1

04:36
Getting LTI model using small angle approximation 2

09:18
Getting LTI model using small angle approximation 3 + Recap

08:19

Model Predictive Control – Intuition – Rocket example

Model Predictive Control – Intro

08:17
Model Predictive Control – Thrust levels

06:56
Model Predictive Control – Cost function

13:48
Model Predictive Control – Cost function having several variables 1

14:07
Model Predictive Control – Cost function having several variables 2

04:28
Model Predictive Control – Cost function weights

06:56
Model Predictive Control – Cost function weights

06:56
Model Predictive Control – Horizon period

10:52
Model Predictive Control – measured VS predicted outputs (Kalman Filter)

09:28
Model Predictive Control – Quadratic VS other cost functions 1

06:37
Model Predictive Control – Quadratic VS other cost functions 2

06:10
Model Predictive Control – Quadratic VS other cost functions 3

08:28
Model Predictive Control – Quadratic VS other cost functions 4

08:21

Model Predictive Control – Mathematical Derivation – autonomous vehicle example

Model Predictive Control – Math – 1

06:47
Model Predictive Control – Math – 1

06:47
Model Predictive Control – Math – 2

11:37
Model Predictive Control – Math – 3

14:12
Model Predictive Control – Math – 4

19:50
Model Predictive Control – Math – 5

12:26
Model Predictive Control – Math – 6

08:25
Model Predictive Control – Math – 7

08:29
Model Predictive Control – Math – 8

10:26
MPC – extra intuition

10:04
Model Predictive Control – Math – 9

02:37
Model Predictive Control – Math – 10

09:09
Model Predictive Control – Math – 11

16:18
Model Predictive Control – Math – 12

05:20
Model Predictive Control – Math – 13

14:29
Model Predictive Control – Math – 14

03:58
Model Predictive Control – Math – 15

08:15
Model Predictive Control – Math – 16

07:38
Model Predictive Control – Math – 18

06:40
Model Predictive Control – Math – 19

08:21
Model Predictive Control – Math – 20

06:51
Model Predictive Control – Math – 21

09:49
Derivation of the gradient of a quadratic vector-matrix form 1

09:31
Derivation of the gradient of a quadratic vector-matrix form 2

05:06
Derivation of the gradient of a quadratic vector-matrix form 3

06:23
Derivation of the gradient of a quadratic vector-matrix form 4

09:46
Derivation of the gradient of a quadratic vector-matrix form 5

11:44

Model Predictive Control – Python Simulation – autonomous vehicle

Intro to (Linux & macOS Terminal) & (Windows Command Prompt)

12:54
Python Simulation Intro

01:02
Python installation instructions – Ubuntu

06:45
Python installation instructions – Windows 10

06:34
Python installation instructions – macOS

08:13
Intro to the simulator

09:17
Recap of the course

06:15
Code explanation 1 – general overview

09:49
Code explanation 2 – a function for storing the initial variables

14:01
Code explanation 3 – a function for generating trajectories

18:31
Code explanation 4 – a function for discrete state space matrices

06:01
Code explanation 5 – a function for generating the MPC cost function matrices

16:23
Code explanation 6 – a function for calculating new states

16:44
Code explanation 7 – the MAIN file 1

15:47
Code explanation 8 – the MAIN file 2

10:55
Code explanation 9 – the MAIN file 3

11:37
Code explanation 10 – the MAIN file 4

03:44
Basic intro to Python animations tools

19:00
Discussing the simulation results

10:57
Discussing the simulation results

10:57
PID VS Model Predictive Control (MPC) 3

09:22

9

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Student Ratings & Reviews

5.0

Total 1 Rating

1 Rating

0 Rating

Ahmad Mushtaq

9 months ago

The best course i completed for control systems. Mark has an exceptional ability to relate all the control theory and mathematical equations to real-life and give students good intuition on how the systems are actually working. I would recommend this course to anyone getting started with control systems.

About Course

Free Applied Control Systems Course: Learn Autonomous Car Technology

What Will You Learn?

Course Content

Intro to Control – PID controller

Course guide

Intro to Control – how to control systems with a controller 1

Intro to Control – how to control systems with a controller 2

Open VS Closed Loop System

Controlling the water tank in a Python simulation

Intro to a proportional controller

Modelling the water tank 1

Modelling the water tank 2

Numerical integration applied to the water tank model

Combining math with the control structure

Water tank simulation – proportional controller

Intro to a PID simulation

Follow up!

PID Modelling the train with forces 1

PID Modelling the train with forces 2

PID Going from system input to system output using numerical integration

PID Magnetic train simulation – proportional controller

PID Proportional controller overshoot explanation 1

PID Proportional controller overshoot explanation 2

PID Proportional controller overshoot explanation 3

PID Intro to Derivative Control

PID Tuning the controller

PID Proportional & Derivative controller & magnetic train simulation in Python

PID Intro to Integral Control

PID Python magnetic train simulation at an inclination angle

PID Mathematical modelling of the train with the inclination angle 1

PID Mathematical modelling of the train with the inclination angle 2

PID Proporti

PID Magnetic train simulation (inclination angle & PID)

Intro to (Linux & macOS Terminal) & (Windows Command Prompt)

Installing the Python environment and its libraries (Linux Ubuntu)

Installing the Python environment and its libraries (Windows 10)

Installing the Python environment and its libraries (macOS)

PID train code explanation 1

PID train code explanation 2

PID train code explanation 3

Short intro to Python animation tools

Quick code & animation explanation (water tanks)

Course Material Download Link

Fundamentals of forces, moments, mass moment of inertia and reference frames

PID VS Model Predictive Control (MPC) 1

Intro to MPC

Getting started with modelling a car

Fundamentals of forces and moments 1

Fundamentals of forces and moments 2

Setting stage for the car’s lateral control 1

Setting stage for the car’s lateral control 2

PID VS Model Predictive Control (MPC) 2

PID VS Model Predictive Control (MPC) 2

Setting stage for the car’s lateral control 3

Setting stage for the car’s lateral control 4

Vehicle modelling for lateral control using equations of motion

Follow up!

The general control structure for the vehicle’s lateral control

Car model VS simplified bicycle model 1

Car model VS simplified bicycle model 2

Car model VS simplified bicycle model 3

Ackerman Steering

Longitudinal & lateral velocities of the bicycle model 1

Longitudinal & lateral velocities of the bicycle model 2

Equations of motion in the lateral direction

Lateral & centripetal acceleration

Centripetal acceleration intuition & mathematical derivation 1

Centripetal acceleration intuition & mathematical derivation 2

Centripetal acceleration intuition & mathematical derivation 3

Modelling the front wheel of the vehicle 1

Rewriting lateral forces in terms of front wheel angles

Modelling the front wheel of the vehicle 2

Modelling the front wheel of the vehicle 3

Modelling the front wheel of the vehicle 4

Vehicle’s state-space & Linear Time Invariant (LTI) model for lateral control

From equations of motion to state-space equations 1

From equations of motion to state-space equations 2

From equations of motion to state-space equations 3

From equations of motion to state-space equations 4