Gain scheduled linear quadratic tracking system tuned optimally by covariance matrix adaption evolutionary strategy for automotive engine coldstart control.
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| Title: | Gain scheduled linear quadratic tracking system tuned optimally by covariance matrix adaption evolutionary strategy for automotive engine coldstart control. |
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| Authors: | Azad, N.1, Mozaffari, A.1 amozaffa@uwaterloo.ca, Hedrick, J.2 |
| Source: | International Journal of Automotive Technology. Apr2017, Vol. 18 Issue 2, p195-207. 13p. |
| Subjects: | Automobile emissions, Covariance matrices, Hydrocarbons, Linear statistical models, Pontryagin's minimum principle |
| Abstract: | In this paper, a gain scheduled linear quadratic tracking system (LQTS) tuned optimally by an evolutionary strategy (ES) is devised to reduce the total tailpipe hydrocarbon ( HC) emissions of an automotive engine over the coldstart period. As the engine's behavior during coldstart operations is nonlinear, the system dynamics is clearly analyzed and represented by a number of separate linear models generated based on a coldstart model verified by experimental data. An independent LQTS is then implemented for each of these linear models. In this way, several control laws are created, and the corresponding gains are calculated for each of the independent control laws. ES is then used to tune the adjustable parameters of LQTSs to calculate the control inputs, namely air/fuel ratio ( AFR) and spark timing (Δ), such that the resulting exhaust gas temperature ( T ) and engine-out HC emissions ( HC ) be close to a set of optimum profiles. This enables the controller reduce the cumulative tailpipe hydrocarbon emissions ( HC ) to the highest possible extent. To demonstrate the acceptable performance of the proposed controller, an optimal controller derived from the Pontryagin's minimum principle (PMP) is also taken into account. Based on the results of the conducted comparative study, it is shown that the proposed control technique has a very good performance, and also, can be easily used for real-time applications, as it consumes a remarkably trivial computational time for calculating the controlling commands. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Automotive Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 120642598 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Gain scheduled linear quadratic tracking system tuned optimally by covariance matrix adaption evolutionary strategy for automotive engine coldstart control. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Azad%2C+N%2E%22">Azad, N.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Mozaffari%2C+A%2E%22">Mozaffari, A.</searchLink><relatesTo>1</relatesTo><i> amozaffa@uwaterloo.ca</i><br /><searchLink fieldCode="AR" term="%22Hedrick%2C+J%2E%22">Hedrick, J.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Automotive+Technology%22">International Journal of Automotive Technology</searchLink>. Apr2017, Vol. 18 Issue 2, p195-207. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Automobile+emissions%22">Automobile emissions</searchLink><br /><searchLink fieldCode="DE" term="%22Covariance+matrices%22">Covariance matrices</searchLink><br /><searchLink fieldCode="DE" term="%22Hydrocarbons%22">Hydrocarbons</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+statistical+models%22">Linear statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Pontryagin's+minimum+principle%22">Pontryagin's minimum principle</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, a gain scheduled linear quadratic tracking system (LQTS) tuned optimally by an evolutionary strategy (ES) is devised to reduce the total tailpipe hydrocarbon ( HC) emissions of an automotive engine over the coldstart period. As the engine's behavior during coldstart operations is nonlinear, the system dynamics is clearly analyzed and represented by a number of separate linear models generated based on a coldstart model verified by experimental data. An independent LQTS is then implemented for each of these linear models. In this way, several control laws are created, and the corresponding gains are calculated for each of the independent control laws. ES is then used to tune the adjustable parameters of LQTSs to calculate the control inputs, namely air/fuel ratio ( AFR) and spark timing (Δ), such that the resulting exhaust gas temperature ( T ) and engine-out HC emissions ( HC ) be close to a set of optimum profiles. This enables the controller reduce the cumulative tailpipe hydrocarbon emissions ( HC ) to the highest possible extent. To demonstrate the acceptable performance of the proposed controller, an optimal controller derived from the Pontryagin's minimum principle (PMP) is also taken into account. Based on the results of the conducted comparative study, it is shown that the proposed control technique has a very good performance, and also, can be easily used for real-time applications, as it consumes a remarkably trivial computational time for calculating the controlling commands. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Automotive Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12239-017-0019-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 195 Subjects: – SubjectFull: Automobile emissions Type: general – SubjectFull: Covariance matrices Type: general – SubjectFull: Hydrocarbons Type: general – SubjectFull: Linear statistical models Type: general – SubjectFull: Pontryagin's minimum principle Type: general Titles: – TitleFull: Gain scheduled linear quadratic tracking system tuned optimally by covariance matrix adaption evolutionary strategy for automotive engine coldstart control. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Azad, N. – PersonEntity: Name: NameFull: Mozaffari, A. – PersonEntity: Name: NameFull: Hedrick, J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 12299138 Numbering: – Type: volume Value: 18 – Type: issue Value: 2 Titles: – TitleFull: International Journal of Automotive Technology Type: main |
| ResultId | 1 |
