Applied Control Systems for Engineers 1 – autonomous vehicle


Applied Control Systems for Engineers 1 - autonomous vehicle

Modelling + state-space systems + PID + Model Predictive Control + Python simulation: autonomous vehicle lateral control

What you’ll learn

  • mathematical modelling of systems
  • reformulating models into state-space equations
  • applying a PID controller to systems (simple magnetic train catching objects)
  • applying Model Predictive Control (MPC) to systems (autonomous car: lane changing maneuvers)


  • Basic Calculus: Functions, Derivatives, Integrals
  • Vector-Matrix multiplication


The world is changing! The technology is changing! The advent of automation in our societies is spreading faster than anyone could have anticipated. At the forefront of our technological progress is autonomy in systems.

Welcome! In this course, you will be exposed to one of the most POWERFUL techniques there are, that are able to guide and control systems precisely and reliably.

You are going to DESIGN, MASTER and APPLY:

  • mathematical models in the form of state-space systems and equations of motion
  • a PID controller to a simple magnetic train that needs to catch objects that randomly fall from the sky
  • a Model Predictive Controller (MPC) to an autonomous vehicle in a simple lane changing maneuver on a straight road at a constant forward speed.

You will LEARN the fundamentals and the logic of Modelling, PID and MPC that will allow you to apply it to other systems you might encounter in the future.

You need 3 things when solving an Engineering problem: INTUITION, MATHEMATICS, CODING! You can’t choose – you really need them all. After this course, you will master Modelling, PID and MPC in all these 3 ways. That’s a promise!

I’m very excited to have you in my course and I can’t wait to teach you what I know.

Let’s get started!

Who this course is for:

  • Science and Engineering students
  • Working Scientists and Engineers
  • Control Engineering enthusiasts

Created by Mark Misin
Last updated 5/2021
English [Auto]

Size: 4.38 GB

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