2019
DOI: 10.1016/j.conengprac.2019.104114
|View full text |Cite
|
Sign up to set email alerts
|

Diesel engine air path control based on neural approximation of nonlinear MPC

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
5

Citation Types

1
8
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
8

Relationship

1
7

Authors

Journals

citations
Cited by 23 publications
(9 citation statements)
references
References 22 publications
1
8
0
Order By: Relevance
“…Therefore, it is more appropriate to apply the reference-tracking based MPC in energy management problems in PHEVs. In current stage, to the best of authors' knowledge, the application of referencetracking based MPC has been widely applied in different fields, such as advanced vehicle dynamic control [20,21], and internal combustion engine (ICE) advance control [22,23]. The homologous application in PHEV energy management, however, is quite rare.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, it is more appropriate to apply the reference-tracking based MPC in energy management problems in PHEVs. In current stage, to the best of authors' knowledge, the application of referencetracking based MPC has been widely applied in different fields, such as advanced vehicle dynamic control [20,21], and internal combustion engine (ICE) advance control [22,23]. The homologous application in PHEV energy management, however, is quite rare.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, there has been an increase in research on control modeling that utilizes machine learning methods such as neural networks and Gaussian processes for model predictive control (MPC); see e.g., [1]- [4]. In the case of complex dynamics, first-principles modeling using physical laws requires advanced knowledge and experience, but there is a possibility that such dynamics can be modeled in a short time without advanced knowledge through data-driven modeling via machine learning methods.…”
Section: Introductionmentioning
confidence: 99%
“…[17][18][19] The development of MPCs has four key issues, such as controller formulation, restrictions on handling, on-line optimization, and the simplicity of the design framework, to deal with the complexity issues. While the MPC has been implemented successfully in several applications, [20][21][22][23][24] the development of controllers for all process types does not involve a prevalent or standard strategy. Finding an appropriate internal model that will predict future output of the process and the choice of an efficient optimization method to generate the optimal trajectory for control are also significant problems in the design framework of the MPC.…”
Section: Introductionmentioning
confidence: 99%
“…To capture the nonlinear dynamic, the nonlinear model predictive control (NMPC), which incorporates nonlinear models for process prediction, is required. [24] Previously, researchers had proposed lipase-catalyzed esterification process models that are based on one factor at a time, which neglected the effect of water activity as one of the input variables. [7] No previous work has been reported considering these two factors at the same time.…”
Section: Introductionmentioning
confidence: 99%