Utilizing optimal control and physical measurements when developing operator assist, automatic functions and autonomous machines
(2017) 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016 p.113-118- Abstract
A method using optimal control results as input to operator assist systems, automatic functions and autonomous construction machine control is presented. This method complements the vast research within autonomy to achieve the most fuel efficient solution from results that are already available from concept evaluation and system optimization in early development. The optimal control results are validated and compared to an extensive empirical study to ensure realization in real applications. The optimal control method is based on dynamic programming and finds the global optimum in regards to fuel efficiency [ton/l] at a given productivity [ton/h]. The wheel loader is used as an example due to the complex nature of the system, where the... (More)
A method using optimal control results as input to operator assist systems, automatic functions and autonomous construction machine control is presented. This method complements the vast research within autonomy to achieve the most fuel efficient solution from results that are already available from concept evaluation and system optimization in early development. The optimal control results are validated and compared to an extensive empirical study to ensure realization in real applications. The optimal control method is based on dynamic programming and finds the global optimum in regards to fuel efficiency [ton/l] at a given productivity [ton/h]. The wheel loader is used as an example due to the complex nature of the system, where the driveline and working hydraulics must work together throughout the work cycle. The main focus in this paper is how to transfer results from the optimal control calculations done offline, with high computational power, to algorithms that can be used online in operator assist systems, automatic functions and autonomous machine control. The primary result is that the method and algorithms presented in this paper works. The secondary results is that the optimal control solution shows around 15% higher fuel efficiency compared to the highest fuel efficiency measured among real operators in the extensive empirical measurement. The operator with the highest measured fuel efficiency has 20-30% higher average fuel efficiency than the fleet implying that the optimal control results, if used in operator assist systems, automatic functions and autonomous machine control, can increase the average fleet fuel efficiency by up to 35-45%, depending on operator and application.
(Less)
- author
- Frank, Bobbie LU
- organization
- publishing date
- 2017-04-05
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- automatic functions, autonomous machines, construction machinery, empirical study, fuel efficiency, Operator assist systems, optimal control
- host publication
- Proceedings - 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016
- article number
- 7893555
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016
- conference location
- Batu Ferringhi, Penang, Malaysia
- conference dates
- 2016-11-25 - 2016-11-27
- external identifiers
-
- scopus:85018998600
- ISBN
- 9781509011780
- DOI
- 10.1109/ICCSCE.2016.7893555
- language
- English
- LU publication?
- yes
- id
- 784d61a8-e60a-449b-8c50-58921292371b
- date added to LUP
- 2017-06-02 14:00:53
- date last changed
- 2025-04-04 14:51:12
@inproceedings{784d61a8-e60a-449b-8c50-58921292371b,
abstract = {{<p>A method using optimal control results as input to operator assist systems, automatic functions and autonomous construction machine control is presented. This method complements the vast research within autonomy to achieve the most fuel efficient solution from results that are already available from concept evaluation and system optimization in early development. The optimal control results are validated and compared to an extensive empirical study to ensure realization in real applications. The optimal control method is based on dynamic programming and finds the global optimum in regards to fuel efficiency [ton/l] at a given productivity [ton/h]. The wheel loader is used as an example due to the complex nature of the system, where the driveline and working hydraulics must work together throughout the work cycle. The main focus in this paper is how to transfer results from the optimal control calculations done offline, with high computational power, to algorithms that can be used online in operator assist systems, automatic functions and autonomous machine control. The primary result is that the method and algorithms presented in this paper works. The secondary results is that the optimal control solution shows around 15% higher fuel efficiency compared to the highest fuel efficiency measured among real operators in the extensive empirical measurement. The operator with the highest measured fuel efficiency has 20-30% higher average fuel efficiency than the fleet implying that the optimal control results, if used in operator assist systems, automatic functions and autonomous machine control, can increase the average fleet fuel efficiency by up to 35-45%, depending on operator and application.</p>}},
author = {{Frank, Bobbie}},
booktitle = {{Proceedings - 6th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2016}},
isbn = {{9781509011780}},
keywords = {{automatic functions; autonomous machines; construction machinery; empirical study; fuel efficiency; Operator assist systems; optimal control}},
language = {{eng}},
month = {{04}},
pages = {{113--118}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Utilizing optimal control and physical measurements when developing operator assist, automatic functions and autonomous machines}},
url = {{http://dx.doi.org/10.1109/ICCSCE.2016.7893555}},
doi = {{10.1109/ICCSCE.2016.7893555}},
year = {{2017}},
}