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)]
References
- Wang H, Liu B, Ping X, An Q. Path tracking control for autonomous vehicles based on an improved MPC. IEEE Access. 2019;7:161064–73. DOI: 10.1109/ACCESS.2019.2944894.
- Hu K, Cheng K. Robust tube-based model predictive control for autonomous vehicle path tracking. IEEE Access. 2022;10:134389–403. DOI: 10.1109/ACCESS.2022.3231443.
- Tang L, Yan F, Zou B, Wang K, Lv C. An improved kinematic model predictive control for high-speed path tracking of autonomous vehicles. IEEE Access. 2020;8:51400–13. DOI: 10.1109/ACCESS.2020.2980188.
- Yang S, Geng C. A longitudinal/lateral coupled neural network model predictive controller for path tracking of self-driving vehicle. IEEE Access. 2023;11:117121–36. DOI: 10.1109/ACCESS.2023.3325326.
- Meshginqalam A, Bauman J. Two-level MPC speed profile optimization of autonomous electric vehicles considering detailed internal and external losses. IEEE Access. 2020;8:206559–70. DOI: 10.1109/ACCESS.2020.3038050.
- Rokonuzzaman M, Mohajer N, Nahavandi S, Mohamed S. Model predictive control with learned vehicle dynamics for autonomous vehicle path tracking. IEEE Access. 2021;9:128233–49. DOI: 10.1109/ACCESS.2021.3112560.
- Kim M, Lee D, Ahn J, Kim M, Park J. Model predictive control method for autonomous vehicles using time-varying and non-uniformly spaced horizon. IEEE Access. 2021;9:86475–87. DOI: 10.1109/ACCESS.2021.3088937.
- Min H, Yang Y, Fang Y, Sun P, Zhao X. Constrained optimization and distributed model predictive control-based merging strategies for adjacent connected autonomous vehicle platoons. IEEE Access. 2019;7:163085–96. DOI: 10.1109/ACCESS.2019.2952049.
- Zhao F, Wu W, Wu Y, Chen Q, Sun Y, Gong J. Model predictive control of soft constraints for autonomous vehicle major lane-changing behavior with time variable model. IEEE Access. 2021;9:89514–25. DOI: 10.1109/ACCESS.2021.3090396.
- Liu H, Sun J, Cheng KWE. A two-layer model predictive path-tracking control with curvature adaptive method for high-speed autonomous driving. IEEE Access. 2023;11:89228–39. DOI: 10.1109/ACCESS.2023.3306239.
Trends in Electrical Engineering
Volume | 14 |
Issue | 01 |
Received | May 20, 2024 |
Accepted | June 12, 2024 |
Published | July 15, 2024 |