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Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller

Author

Listed:
  • 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)

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.

Suggested Citation

  • Raymundo Cordero & Thyago Estrabis & Gabriel Gentil & Matheus Caramalac & Walter Suemitsu & João Onofre & Moacyr Brito & Juliano dos Santos, 2022. "Tracking and Rejection of Biased Sinusoidal Signals Using Generalized Predictive Controller," Energies, MDPI, vol. 15(15), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5664-:d:880226
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    References listed on IDEAS

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    1. Jorge Rivera & Susana Ortega-Cisneros & Florentino Chavira, 2019. "Sliding Mode Output Regulation for a Boost Power Converter," Energies, MDPI, vol. 12(5), pages 1-17, March.
    2. Carlos E. Prieto Cerón & Luís F. Normandia Lourenço & Juan S. Solís-Chaves & Alfeu J. Sguarezi Filho, 2022. "A Generalized Predictive Controller for a Wind Turbine Providing Frequency Support for a Microgrid," Energies, MDPI, vol. 15(7), pages 1-20, April.
    3. Thuy Vi Tran & Seung-Jin Yoon & Kyeong-Hwa Kim, 2018. "An LQR-Based Controller Design for an LCL-Filtered Grid-Connected Inverter in Discrete-Time State-Space under Distorted Grid Environment," Energies, MDPI, vol. 11(8), pages 1-28, August.
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