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Model predictive control (MPC) is a flexible and multivariable control technique with better dynamic performance than linear control. However, MPC is sensitive to parametric mismatches that reduce its control capabilities. In this paper, we present a new method of improving the robustness of MPC to filter parameter variations/mismatches by easily implementable parameter estimation. Furthermore, we extend the proposed technique for wider operating conditions by novel neuro-fuzzy estimation. The results, which are demonstrated by both simulations and real-time hardware-in-the-loop tests, show a steady-state parameter estimation accuracy of 95%, and at least 20% improvement in total harmonic distortion (THD) than conventional non-adaptive MPC under parameter mismatches up to 50% of the nominal values.
Oluleke Babayomi; Zhenbin Zhang; Yu Li; Ralph Kennel. Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters. Sustainability 2021, 13, 7038 .
AMA StyleOluleke Babayomi, Zhenbin Zhang, Yu Li, Ralph Kennel. Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters. Sustainability. 2021; 13 (13):7038.
Chicago/Turabian StyleOluleke Babayomi; Zhenbin Zhang; Yu Li; Ralph Kennel. 2021. "Adaptive Predictive Control with Neuro-Fuzzy Parameter Estimation for Microgrid Grid-Forming Converters." Sustainability 13, no. 13: 7038.