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 course, you will learn about important topics such as creating a Python simulated environment, self-modeling systems, PID controller, Model Predictive Control, and so on. In designing 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 autonomous vehicle on a two-dimensional environment by taking advantage of the bike ( 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: YodemiInstructor: Mark Misin Engineering Ltd
Language: English
Level: Average
Number of Courses: 112
Training Duration: 13 hours and 33 minutes
Course topics
Prerequisites for Applied Control Systems 2: autonomous cars (360 tracking)
Basic Calculus: Functions, Derivatives, IntegralsVector-Matrix multiplication
Udemy course: Applied Control Systems 1: autonomous cars (Math + PID + MPC)
Course pictures
Applied Control Systems 2: autonomous cars (360 tracking)
Video viewerMedia error: Format (s) not supported or source (s) not found
Download file: https://dl.downloadly.ir/Files/Elearning/Sample/Applied_Control_Systems_2_autonomous_cars_360_tracking_Downloadly.ir.mp4?_=1
00:00
00:00
Installation guide
After Extract, watch with your favorite Player.English subtitle
Quality: 720p
download link
Download Part 1 - 2 GBDownload Part 2 - 2 GB
Download Section 3 - 1.68 GB