Model Predictive Control for Autonomous Driving Vehicles
| Autor: | Trieu Minh Vu, Reza Moezzi, Jindrich Cyrus, Jaroslav Hlava |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
TK7800-8360
Computer Networks and Communications Computer science nonlinear model predictive control optimal control action Angular velocity hard and softened constraints Optimal control Track (rail transport) Field (computer science) Computer Science::Robotics Tracking error Model predictive control Hardware and Architecture Control and Systems Engineering Control theory Control system Signal Processing trajectory tracking tracking error Electronics Electrical and Electronic Engineering |
| Zdroj: | Electronics Volume 10 Issue 21 Electronics, Vol 10, Iss 2593, p 2593 (2021) |
| ISSN: | 2079-9292 |
| Popis: | The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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