A dynamic optimization approach to adaptive control for the four-bar linkage mechanism

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

© Springer Nature Switzerland AG 2019. In this work, a control strategy for the speed regulation of the four-bar linkage mechanism is presented. This strategy is based on the dynamic optimization approach to adaptive control. In this approach, a dynamic optimization problem is stated and solved on-line using an optimizer to find the best set of control parameters. A novel variant of the Differential Evolution optimizer with an optimum tracking mechanism which allows to maintain the diversity of solutions is proposed in order to handle the changing best solution of the dynamic optimization problem. A full statistical analysis is used to prove the effectiveness of the proposed strategy. The performance of this strategy is tested in simulation and is compared with a PI controller.
Original languageAmerican English
Title of host publicationA dynamic optimization approach to adaptive control for the four-bar linkage mechanism
Pages892-906
Number of pages801
DOIs
StatePublished - 1 Jan 2018
EventAdvances in Intelligent Systems and Computing -
Duration: 1 Jan 2018 → …

Publication series

NameAdvances in Intelligent Systems and Computing
Volume869
ISSN (Print)2194-5357

Conference

ConferenceAdvances in Intelligent Systems and Computing
Period1/01/18 → …

Fingerprint

Switzerland

Cite this

Rodríguez-Molina, A., Villarreal-Cervantes, M. G., & Aldape-Pérez, M. (2018). A dynamic optimization approach to adaptive control for the four-bar linkage mechanism. In A dynamic optimization approach to adaptive control for the four-bar linkage mechanism (pp. 892-906). (Advances in Intelligent Systems and Computing; Vol. 869). https://doi.org/10.1007/978-3-030-01057-7_66
Rodríguez-Molina, Alejandro ; Villarreal-Cervantes, Miguel Gabriel ; Aldape-Pérez, Mario. / A dynamic optimization approach to adaptive control for the four-bar linkage mechanism. A dynamic optimization approach to adaptive control for the four-bar linkage mechanism. 2018. pp. 892-906 (Advances in Intelligent Systems and Computing).
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abstract = "{\circledC} Springer Nature Switzerland AG 2019. In this work, a control strategy for the speed regulation of the four-bar linkage mechanism is presented. This strategy is based on the dynamic optimization approach to adaptive control. In this approach, a dynamic optimization problem is stated and solved on-line using an optimizer to find the best set of control parameters. A novel variant of the Differential Evolution optimizer with an optimum tracking mechanism which allows to maintain the diversity of solutions is proposed in order to handle the changing best solution of the dynamic optimization problem. A full statistical analysis is used to prove the effectiveness of the proposed strategy. The performance of this strategy is tested in simulation and is compared with a PI controller.",
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Rodríguez-Molina, A, Villarreal-Cervantes, MG & Aldape-Pérez, M 2018, A dynamic optimization approach to adaptive control for the four-bar linkage mechanism. in A dynamic optimization approach to adaptive control for the four-bar linkage mechanism. Advances in Intelligent Systems and Computing, vol. 869, pp. 892-906, Advances in Intelligent Systems and Computing, 1/01/18. https://doi.org/10.1007/978-3-030-01057-7_66

A dynamic optimization approach to adaptive control for the four-bar linkage mechanism. / Rodríguez-Molina, Alejandro; Villarreal-Cervantes, Miguel Gabriel; Aldape-Pérez, Mario.

A dynamic optimization approach to adaptive control for the four-bar linkage mechanism. 2018. p. 892-906 (Advances in Intelligent Systems and Computing; Vol. 869).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Rodríguez-Molina A, Villarreal-Cervantes MG, Aldape-Pérez M. A dynamic optimization approach to adaptive control for the four-bar linkage mechanism. In A dynamic optimization approach to adaptive control for the four-bar linkage mechanism. 2018. p. 892-906. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-01057-7_66