Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller

Cordero, Raymundo; Estrabis, Thyago; Gentil, Gabriel; Caramalac, Matheus; Suemitsu, Walter; Onofre, João; Brito, Moacyr; dos Santos, Juliano

doi:10.3390/en15155664

Title: Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller

Journal Article · Thu Aug 04 00:00:00 EDT 2022 · Energies

DOI:https://doi.org/10.3390/en15155664· OSTI ID:1885219

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Federal University of Mato Grosso do Sul, Campo Grande (Brazil)
Federal University of Rio de Janeiro, Rio de Janeiro (Brazil)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

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.

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Research Organization:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1885219

Journal Information:: Energies, Vol. 15, Issue 15; ISSN 1996-1073

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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