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Udemy – Applied Control Systems 2: autonomous cars (360 tracking) 2021-10

2 years ago 147

Description

Applied Control Systems 2: autonomous cars (360 tracking) is the second part of the Applied Control Systems training series that introduces you to the technology of self-driving cars. In this training course, you will learn information on important topics such as creating a Python simulated environment, modeling self-driving systems, PID controller, Model Predictive Control, and more. In the design of self-driving cars, the main challenge is to keep the car steady in the right direction and positioning to move in the direction of the target. For this purpose, values such as acceleration, initial speed and steering angle of the car should be set as accurately as possible, and a slight difference can lead to unwanted results. These values must have a reasonable maximum and minimum limit so that the car can operate optimally on the road.

Mark Misin, the instructor of this course, works in the field of robotics and aerospace and intends to transfer his experiences to those who are interested. In the first part, we succeeded in using the MPC algorithm to put the car in a straight line on automatic mode and change lanes. Finally, by optimizing the car angle, you were able to turn your nonlinear model into a time-invariant linear system (LTI) and make it slightly more flexible relative to the road direction. This change allows the car to have better navigation in general, but also imposes a number of limitations. In the second part, we will go further than before and by using the linear variable parameters, we will turn our ordinary MPC controller into a non-linear and flexible system that will be able to track the path.

What you will learn in Applied Control Systems 2: autonomous cars (360 tracking)

Modify the original MPC and convert it to a fixed time linear system (LTI)
Familiarity with the equation of motion and the form of state space
Familiarity with MPC controllers and limiters and implementation of these systems in cars
Mathematical and computational modeling of self-driving cars in a two-dimensional environment using bicycle model
Familiarity with linear MPCs and their implementation in nonlinear systems using LPV formulation
Simulation of car control loops using Python

Course specifications

Publisher: Udemy
Instructors: Mark Misin Engineering Ltd
Language: English
Level: Intermediate
Number of Lessons: 112
Duration: 13 hours and 33 minutes