Description
Applied Control Systems 2: autonomous cars (360 tracking) is the second part of the Applied Control Systems educational series, which introduces you to the technology of self-driving cars. In this training course, you will learn about important topics such as creating a simulated environment with Python, modeling autonomous systems, PID controller, Model Predictive Control, etc. In the design of self-driving cars, the main challenge is to keep the car steady on the track and correctly positioning it to move in the direction of the target. For this purpose, values such as acceleration, initial speed and steering angle of the car must be set in the most accurate way 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 perform optimally on the road.
Mark Misin, the instructor of this training course, works in the fields of robotics and aerospace and plans to share his experiences with those interested. In the first part, we managed to use the MPC algorithm to put the car on automatic mode and change lanes on the straight road. In the end, by optimizing the angle of the car, you were able to turn your nonlinear model into a linear time-invariant system (LTI) and make it slightly flexible with respect to the direction of the road. This change makes the car have better navigation in general, but at the same time, it also causes some 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 flexible nonlinear system that will be able to track the path.
What you will learn in the Applied Control Systems 2: autonomous cars (360 tracking) course:
- Modifying and improving the basic MPC and turning it into a linear constant-time (LTI) system.
- Familiarity with the equation of motion and State space form
- Familiarity with controllers and limiters MPC and implementing these systems in self-driving cars
- Mathematical and computational modeling of self-driving cars in a two-dimensional environment using a bicycle model (bicycle model)
- Meet MPC linear systems and their implementation in non-linear systems using formulations LPV
- Simulation of self-driving car control loop using Python
Course details
Publisher: Yodmi
teacher: Mark Misin Engineering Ltd
English language
Education level: Intermediate
Number of courses: 112
Training duration: 13 hours and 33 minutes
Course headings
Applied Control Systems 2 course prerequisites: autonomous cars (360 tracking)
Basic Calculus: Functions, Derivatives, Integrals
Vector-Matrix multiplication
Udemy course: Applied Control Systems 1: autonomous cars (Math + PID + MPC)
Course images
Applied Control Systems 2 course introduction video: autonomous cars (360 tracking)
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
download link
Password file(s): www.downloadly.ir
Size
5.68 GB
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