Deep-reinforcement-learning-based controller design for pantograph and catenary system.
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| Title: | Deep-reinforcement-learning-based controller design for pantograph and catenary system. |
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| Authors: | Sharma, Rohini1 (AUTHOR) rs2456sharma@gmail.com, Mahajan, Priya1 (AUTHOR) priyamahajan.eed@gmail.com, Garg, Rachana1 (AUTHOR) rachana16100@yahoo.co.in |
| Source: | Sādhanā: Academy Proceedings in Engineering Sciences. Jun2025, Vol. 50 Issue 2, p1-17. 17p. |
| Subjects: | Long short-term memory, Error functions, Catenary, Motor vehicle springs & suspension, Pantograph |
| Abstract: | The articulated suspension system of a pantograph and catenary system has a power collection junction, i.e., a contact network, between the pantograph and the catenary. For stable current collection and maintaining control contact force, the junction should be reliable and safe with respect to external disturbances and irregularity at the contact point, which may vary due to the speed of the train. This study proposes a novel fuzzy-based controller to control the contact force. The proposed controller, fuzzy-based fractional order proportional integral derivative (F2OPID), uses fuzziness to evaluate control contact force. The error functions of the proposed controller are monitored by a bilateral long short-term memory network. The gains of the F2OPID controller are evaluated using an Aquila optimizer. The system is analyzed on Matlab platform based on variations in train speed, stiffness coefficient, and length of the span. From the analysis, it has been observed that oscillation in contact force has been reduced using the proposed controller compared with other methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Sādhanā: Academy Proceedings in Engineering Sciences 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: 184788217 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Deep-reinforcement-learning-based controller design for pantograph and catenary system. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sharma%2C+Rohini%22">Sharma, Rohini</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rs2456sharma@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Mahajan%2C+Priya%22">Mahajan, Priya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> priyamahajan.eed@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Garg%2C+Rachana%22">Garg, Rachana</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rachana16100@yahoo.co.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sādhanā%3A+Academy+Proceedings+in+Engineering+Sciences%22">Sādhanā: Academy Proceedings in Engineering Sciences</searchLink>. Jun2025, Vol. 50 Issue 2, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Error+functions%22">Error functions</searchLink><br /><searchLink fieldCode="DE" term="%22Catenary%22">Catenary</searchLink><br /><searchLink fieldCode="DE" term="%22Motor+vehicle+springs+%26+suspension%22">Motor vehicle springs & suspension</searchLink><br /><searchLink fieldCode="DE" term="%22Pantograph%22">Pantograph</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The articulated suspension system of a pantograph and catenary system has a power collection junction, i.e., a contact network, between the pantograph and the catenary. For stable current collection and maintaining control contact force, the junction should be reliable and safe with respect to external disturbances and irregularity at the contact point, which may vary due to the speed of the train. This study proposes a novel fuzzy-based controller to control the contact force. The proposed controller, fuzzy-based fractional order proportional integral derivative (F2OPID), uses fuzziness to evaluate control contact force. The error functions of the proposed controller are monitored by a bilateral long short-term memory network. The gains of the F2OPID controller are evaluated using an Aquila optimizer. The system is analyzed on Matlab platform based on variations in train speed, stiffness coefficient, and length of the span. From the analysis, it has been observed that oscillation in contact force has been reduced using the proposed controller compared with other methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sādhanā: Academy Proceedings in Engineering Sciences 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/s12046-025-02692-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Long short-term memory Type: general – SubjectFull: Error functions Type: general – SubjectFull: Catenary Type: general – SubjectFull: Motor vehicle springs & suspension Type: general – SubjectFull: Pantograph Type: general Titles: – TitleFull: Deep-reinforcement-learning-based controller design for pantograph and catenary system. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sharma, Rohini – PersonEntity: Name: NameFull: Mahajan, Priya – PersonEntity: Name: NameFull: Garg, Rachana IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02562499 Numbering: – Type: volume Value: 50 – Type: issue Value: 2 Titles: – TitleFull: Sādhanā: Academy Proceedings in Engineering Sciences Type: main |
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