Hierarchical Predictive Control for Ground-Vehicle Maneuvering
(2015) American Control Conference, 2015 p.2771-2776- Abstract
- This paper presents a hierarchical approach to feedback-based trajectory generation for improved vehicle autonomy. Hierarchical vehicle-control structures have been used before—for example, in electronic stability control systems, where a low-level control loop tracks high-level references. Here, the control structure includes a nonlinear vehicle model already at the high level to generate optimization-based references. A nonlinear model-predictive control (MPC) formulation, combined with a linearized MPC acting as a backup controller, tracks these references by allocating torque and steer commands. With this structure the two control layers have a physical coupling, which makes it easier for the low-level loop to track the references.... (More)
- This paper presents a hierarchical approach to feedback-based trajectory generation for improved vehicle autonomy. Hierarchical vehicle-control structures have been used before—for example, in electronic stability control systems, where a low-level control loop tracks high-level references. Here, the control structure includes a nonlinear vehicle model already at the high level to generate optimization-based references. A nonlinear model-predictive control (MPC) formulation, combined with a linearized MPC acting as a backup controller, tracks these references by allocating torque and steer commands. With this structure the two control layers have a physical coupling, which makes it easier for the low-level loop to track the references. Simulation results show improved performance over an approach based on linearized MPC, as well as feasibility for online implementations. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5031551
- author
- Berntorp, Karl and Magnusson, Fredrik LU
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the 2015 American Control Conference
- pages
- 2771 - 2776
- conference name
- American Control Conference, 2015
- conference location
- Chicago, IL, United States
- conference dates
- 2015-07-01 - 2015-07-03
- external identifiers
-
- scopus:84940902577
- project
- LCCC
- Numerical and Symbolic Algorithms for Dynamic Optimization
- language
- English
- LU publication?
- yes
- id
- 24870a96-55f6-497b-95cb-2a73179db251 (old id 5031551)
- date added to LUP
- 2016-04-04 14:36:28
- date last changed
- 2024-06-04 15:07:18
@inproceedings{24870a96-55f6-497b-95cb-2a73179db251,
abstract = {{This paper presents a hierarchical approach to feedback-based trajectory generation for improved vehicle autonomy. Hierarchical vehicle-control structures have been used before—for example, in electronic stability control systems, where a low-level control loop tracks high-level references. Here, the control structure includes a nonlinear vehicle model already at the high level to generate optimization-based references. A nonlinear model-predictive control (MPC) formulation, combined with a linearized MPC acting as a backup controller, tracks these references by allocating torque and steer commands. With this structure the two control layers have a physical coupling, which makes it easier for the low-level loop to track the references. Simulation results show improved performance over an approach based on linearized MPC, as well as feasibility for online implementations.}},
author = {{Berntorp, Karl and Magnusson, Fredrik}},
booktitle = {{Proceedings of the 2015 American Control Conference}},
language = {{eng}},
pages = {{2771--2776}},
title = {{Hierarchical Predictive Control for Ground-Vehicle Maneuvering}},
url = {{https://lup.lub.lu.se/search/files/7670482/5031578.pdf}},
year = {{2015}},
}