explanation
Applied Control Systems 2: Autonomous Vehicles (360-Degree Tracking) is the second part of the Applied Control Systems training series that introduces autonomous vehicle technology. In this course, you will learn information about important topics such as creating a Python simulation environment, modeling autonomous driving systems, PID controllers, model predictive control, and more. The most important challenge in designing self-driving cars is keeping the car stable and positioned to move in the right direction. To achieve this, the vehicle’s acceleration, initial speed, steering angle, etc. values must be set as accurately as possible, and slight differences may lead to unwanted results. These values should have reasonable maximum and minimum limits to allow the car to perform optimally on the road.
Mark Misin, the instructor for this course, works in the robotics and aerospace fields and would like to convey his experience to those who are interested. In the first part, he succeeded in keeping the car in a straight line and changing lanes in automatic mode using the MPC algorithm. Finally, he was able to optimize the car angle, turning the non-linear model into a time-invariant linear system (LTI) and making it slightly more flexible relative to road direction. These changes allowed the car to have better navigation overall, but also introduced a number of limitations. In the second part, we will go further than before and turn a typical MPC controller with linear variable parameters into a non-linear and flexible system capable of tracking paths.
What you will learn in Applied Control System 2: Self-driving cars (360-degree tracking)
- Modify the original MPC and convert it to a fixed-time linear system (LTI).
- Knowledge of equations of motion and shape of state space
- Knowledge of MPC controllers and limiters and how to implement these systems in your car.
- Mathematical and computational modeling of self-driving cars in a two-dimensional environment using a bicycle model
- Knowledge of linear MPC and its implementation in nonlinear systems using LPV formulation.
- Automotive control loop simulation using Python
Course specifications
Publisher: Udemy
teacher: Mark Mishin Engineering Ltd
Language:English
Level: Intermediate
Number of classes: 112
Duration: 13 hours 33 minutes
course topic
Application control system 2: Autonomous vehicle (360-degree tracking) prerequisites
Basic Calculus: Functions, Derivatives, Integrals
Vector-Matrix Multiplication
Udemy Courses: Application control system 1: Autonomous vehicle (Math + PID + MPC)
movie
Application control system 2: Self-driving car (360 tracking) introduction video
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