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

Hancheol Cho, Giorgio Bacelli and Ryan G. Coe
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Hancheol Cho: Department of Mechanical Engineering, Bradley University, Peoria, IL 61625, USA
Giorgio Bacelli: Water Power Technologies Department, Sandia National Laboratories, Albuquerque, NM 87185, USA
Ryan G. Coe: Water Power Technologies Department, Sandia National Laboratories, Albuquerque, NM 87185, USA

Energies, 2019, vol. 12, issue 21, 1-18

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.

Keywords: model predictive control (MPC); constrained linear systems; controller tuning; wave energy converter (WEC) (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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