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Model Predictive Control Tuning by Inverse Matching for a Wave Energy Converter

Author

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  • Hancheol Cho

    (Department of Mechanical Engineering, Bradley University, Peoria, IL 61625, USA
    These authors contributed equally to this work.)

  • Giorgio Bacelli

    (Water Power Technologies Department, Sandia National Laboratories, Albuquerque, NM 87185, USA
    These authors contributed equally to this work.)

  • Ryan G. Coe

    (Water Power Technologies Department, Sandia National Laboratories, Albuquerque, NM 87185, USA
    These authors contributed equally to this work.)

Abstract

This paper investigates the application of a method to find the cost function or the weight matrices to be used in model predictive control (MPC) such that the MPC has the same performance as a predesigned linear controller in state-feedback form when constraints are not active. This is potentially useful when a successful linear controller already exists and it is necessary to incorporate the constraint-handling capabilities of MPC. This is the case for a wave energy converter (WEC), where the maximum power transfer law is well-understood. In addition to solutions based on numerical optimization, a simple analytical solution is also derived for cases with a short prediction horizon. These methods are applied for the control of an empirically-based WEC model. The results show that the MPC can be successfully tuned to follow an existing linear control law and to comply with both input and state constraints, such as actuator force and actuator stroke.

Suggested Citation

  • Hancheol Cho & Giorgio Bacelli & Ryan G. Coe, 2019. "Model Predictive Control Tuning by Inverse Matching for a Wave Energy Converter," Energies, MDPI, vol. 12(21), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4158-:d:282045
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    References listed on IDEAS

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    1. Giorgio Bacelli & Ryan G. Coe & David Patterson & David Wilson, 2017. "System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling," Energies, MDPI, vol. 10(4), pages 1-33, April.
    2. Li, Guang & Weiss, George & Mueller, Markus & Townley, Stuart & Belmont, Mike R., 2012. "Wave energy converter control by wave prediction and dynamic programming," Renewable Energy, Elsevier, vol. 48(C), pages 392-403.
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    Cited by:

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    2. Coe, Ryan G. & Bacelli, Giorgio & Forbush, Dominic, 2021. "A practical approach to wave energy modeling and control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).

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