Applied Control Systems 2 autonomous cars 360 tracking

system modeling + state space systems + Model Predictive Control + MPC constraints + Python simulation: autonomous cars

What you’ll learn:
revision of Model Predictive Control for Linear Time Invariant (LTI) systems
mathematical modeling of an autonomous car on a 2D X-Y plane using the bicycle model
going from the vehicle’s equations of motion to its state space form
mastering & applying linear Model Predictive Control (MPC) to a nonlinear system using Linear Parameter Varying (LPV) formulation
mastering & applying Model Predictive Control (MPC) constraints to the autonomous car
simulating the control loop for the autonomous car in Python including the Model Predictive Control (MPC) controller and its constraints

Requirements
Basic Calculus: Functions, Derivatives, Integrals
Vector-Matrix multiplication
Udemy course: Applied Control Systems 1: autonomous cars (Math + PID + MPC)

Description
How do you make autonomous cars track a general trajectory on a 2D plane and how do you make sure that the velocities, accelerations and steering wheel angles of the autonomous cars stay within their realistic minimum and maximum values?

My name is Mark. I’m an Aerospace & Robotics Engineer and in this course, I will give you intuition, Mathematics and Python implementation for all that.

This course is a direct continuation to the course “Applied Control Systems 1: autonomous cars: Math + PID + MPC. In the previous course, the Model Predictive Control (MPC) algorithm only allowed the autonomous cars to change lanes on a straight road. We applied a small angle approximation to convert our nonlinear model to linear time invariant (LTI). It made our lives easier but it also restricted our Model Predictive Control algorithm.

In this course however, we will remove that simplification and I will show you how you can apply a linear MPC controller to a nonlinear system by putting it in a Linear Parameter Varying form first. With this highly popular technique, your car will be able to track a general 2D trajectory.

In addition, you will learn how to use quadratic solvers such as qpsolvers & quadprog to apply MPC constraints to autonomous cars. In most control problems, you have to consider constraints in order to keep your system within reasonable values.

The knowledge that you get from this course is universal and can be applied to so many systems in control systems engineering.

Take a look at some of my free preview videos and if you like what you see, then ENROLL NOW, and let’s get started.

Hope to see you inside!

Who this course is for
Science and Engineering students
Working Scientists and Engineers
Control Engineering enthusiasts

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