Model predictive control (MPC) lacks an integrating element. Thus, parameter mismatches can deteriorate its steady-state performance. To address this issue and enhance the robustness of MPC, analternative formulation of the prediction model is discussed in this paper. This model introduces an integrator to the optimization problem without increasing its size and consequently its computational complexity. An in-depth analysis of the effect of parameter mismatches on the control performance is performed when both the conventional and the proposed prediction model are used. Specifically, the aforementioned analysis is carried out for a range of switching frequencies as well as prediction horizon lengths, while a permanent magnet synchronous motor (PMSM) drive is used as a case study.

Robustness Analysis of Long-Horizon Direct Model Predictive Control: Permanent Magnet Synchronous Motor Drives

Ortombina L.
;
Zigliotto M.
2020

Abstract

Model predictive control (MPC) lacks an integrating element. Thus, parameter mismatches can deteriorate its steady-state performance. To address this issue and enhance the robustness of MPC, analternative formulation of the prediction model is discussed in this paper. This model introduces an integrator to the optimization problem without increasing its size and consequently its computational complexity. An in-depth analysis of the effect of parameter mismatches on the control performance is performed when both the conventional and the proposed prediction model are used. Specifically, the aforementioned analysis is carried out for a range of switching frequencies as well as prediction horizon lengths, while a permanent magnet synchronous motor (PMSM) drive is used as a case study.
2020
2020 IEEE 21st Workshop on Control and Modeling for Power Electronics, COMPEL 2020
978-1-7281-7160-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3373380
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