Model Predictive Control Tuning by Inverse Matching for a Wave Energy Converter
- Bradley Univ., Peoria, IL (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
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.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W); USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1595034
- Report Number(s):
- SAND-2019-15027J; ENERGA; 682231
- Journal Information:
- Energies, Vol. 12, Issue 21; ISSN 1996-1073
- Publisher:
- MDPI AGCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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