Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller

Raymundo Cordero, Thyago Estrabis, Gabriel Gentil, Matheus Caramalac, Walter Suemitsu, João Onofre, Moacyr Brito and Juliano dos Santos
Additional contact information
Raymundo Cordero: Electrical Engineering Graduation Program, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil
Thyago Estrabis: COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, RJ, Brazil
Gabriel Gentil: COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, RJ, Brazil
Matheus Caramalac: Electrical Engineering Graduation Program, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil
Walter Suemitsu: COPPE, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, RJ, Brazil
João Onofre: Oak Ridge National Laboratory—ORNL, Oak Ridge, TN 37830, USA
Moacyr Brito: Electrical Engineering Graduation Program, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil
Juliano dos Santos: Electrical Engineering Graduation Program, Federal University of Mato Grosso do Sul, Campo Grande 79070-900, MS, Brazil

Energies, 2022, vol. 15, issue 15, 1-13

Abstract: Some novel applications require the tracking/rejection of biased sinusoidal reference/distur-bances. According to the internal model principle (IMP), a controller must embed the model of a biased sinusoidal signal to track references and also reject perturbations modeled through the aforementioned signal. However, the design of that kind of controller is not straightforward, especially when they are implemented in digital processors. This paper presents a controller, based on generalized predictive control (GPC), designed for tracking/rejection of biased sinusoidal signals. In general, GPC is based on the prediction of the plant responses through an augmented prediction model. The proposed approach develops an augmented model that predicts the future errors. The prediction model and the control law used in the proposed approach embed the discrete-time model of a biased sinusoidal signal. Thus, the proposed controller can track/reject biased sinusoidal references/disturbances. The predicted errors and the future inputs of the proposed augmented model are used to define the cost function that measures the control performance. An optimization technique was applied to obtain the solution of the cost function, which is the optimal sequence of future model inputs that allows defining the control law. Experimental tests prove that the proposed controller can asymptotically track and reject biased sinusoidal signals.

Keywords: biased sinusoidal signal; disturbance rejection; internal model principle; predictive control tracking (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/15/5664/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/15/5664/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:15:p:5664-:d:880226

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().