Description
Applied Control Systems 2: Self-Driving Cars (Tracking 360) is the second part of the Applied Control Systems training series that introduces you to self-driving car technology. In this training, you will learn about important topics such as creating a Python simulated environment, modeling autonomous systems, PID controller, model predictive control, and more. In designing self-driving cars, the main challenge is to keep the car stable in the right direction and to position itself 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 precisely as possible, and a slight difference can lead to undesirable results. These values should have a reasonable upper and lower limit for the car to perform optimally on the road.
Mark Misin, the instructor for this course, works in the field of robotics and aerospace and intends to pass on his experiences to those who are interested. In the first part, we managed to use the MPC algorithm to put the car in a straight line in automatic mode and change lanes. Finally, by optimizing the angle of the car, you were able to turn your nonlinear model into a linear time invariant (LTI) system and make it slightly more flexible with respect to the direction of the road. 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 using linear variable parameters, we will transform our ordinary MPC controller into a non-linear and flexible system which will be able to follow the path.
What you’ll learn in Applied Control Systems 2: Self-Driving Cars (360° Tracking)
- Modify the original MPC and convert it to a linear fixed time (LTI) system
- Familiarity with the equation of motion and the shape of the state space
- Familiarity with MPC controllers and limiters and the implementation of these systems in cars
- Mathematical and computer modeling of self-driving cars in a two-dimensional environment using a bicycle model
- Familiarity with linear MPCs and their implementation in nonlinear systems using the LPV formulation
- Simulating Car Control Loops Using Python
Course specifications
Publisher: Udemy
Instructors: Mark Misin Engineering Ltd.
French language
Intermediate level
Number of lessons: 112
Duration: 13 hours and 33 minutes
Course topics
Applied Control Systems 2: autonomous cars (360 tracking) Prerequisites
Basic calculation: functions, derivatives, integrals
Vector-matrix multiplication
Udemy courses: Applied Control Systems 1: Autonomous Cars (Math + PID + MPC)
Pictures
Applied Control Systems 2: Self-Driving Cars Overview Video (360 Degree Tracking)
Installation guide
After ripping, watch with your favorite player.
english subtitle
Quality: 720p
Download link
File password(s): ngaur.com
Cut
5.68 GB