An improved model predictive control approach for fuel efficiency optimization of vessel propulsion systems
Control Engineering Practice, ISSN: 0967-0661, Vol: 109, Page: 104749
2021
- 2Citations
- 9Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
This paper studies the fuel efficiency improvement issues for the vessel propulsion systems (VPSs). Specifically, the fuel efficiency is optimized by a novel model predictive control (MPC) approach. Our study consists of two parts. In the first part, using only the measurable data, the predictive model is constructed with the aid of the sparse regression method (SRM). The second part is devoted to improving the fuel efficiency of the VPS based on the suboptimality estimate of the MPC scheme. A complete design method of the model predictive controller for the VPSs is given in this paper, which consists of the data-driven system identification, controller formulation and on-line closed-loop performance improvement. A case study on a very large ore carrier (VLOC) propulsion system is given in the end to show that the proposed approach achieves better fuel efficiency than the traditional MPC scheme.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0967066121000265; http://dx.doi.org/10.1016/j.conengprac.2021.104749; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85099784245&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0967066121000265; https://dx.doi.org/10.1016/j.conengprac.2021.104749
Elsevier BV
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