Neeraj Vijay,
Farsana Muhammed,
- M.Tech. Scholar Department of Electrical and Electronics Engineering, Thangal Kunju Musaliar College of Engineering, Kollam Kerala India
- Assistant Professor Department of Electrical and Electronics Engineering, Thangal Kunju Musaliar College of Engineering, Kollam Kerala India
Abstract
Autonomous vehicles, which are often known as self-driving automobiles or driverless cars, are vehicles that can navigate and operate without human intervention. They require efficient controllers capable of handling complexities, with reduced computational costs, and should handle multiple inputs and outputs simultaneously. Model predictive control (MPC) possesses all these characteristics which means it can be utilized effectively for the same purpose. MPC for autonomous vehicles proposes various ways of achieving efficient strategies for major path-tracking problems of autonomous vehicles (AV) focusing on their design, implementation, and performance across various scenarios. The experimental simulation results, the inferences, and the future scope of the work are also specified. This review paper encapsulates the various MPCs used in various control problems related to AV. Model Predictive Control (MPC) is an algorithm that has proven to be an effective tool for managing the dynamic and complex situations encountered by self-driving cars. This article explores the fundamentals of MPC, its applications in autonomous driving, and the challenges and potential advancements in this field of technology.
Keywords: Model predictive control (MPC), autonomous vehicles (AV), path tracking, optimization function, neural network (NN)
[This article belongs to Trends in Electrical Engineering(tee)]
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Trends in Electrical Engineering
| Volume | 14 |
| Issue | 01 |
| Received | May 20, 2024 |
| Accepted | June 12, 2024 |
| Published | July 15, 2024 |