Model-Free Simplified Predictive Current Control of PMSM Drive with Ultra-Local Model-Based EKF

Zerdali, Emrah; Wheeler, Patrick

doi:10.1109/gpecom52585.2021.9587527

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…When the parameter mismatch is relatively small, ( 15) is still guaranteed to hold, that is, the system is stable, but the control performance will become worse. When the parameter mismatch is large, (15) will be not valid, resulting in an unstable system. Therefore, when the parameters are not matched, the conventional method control effect is poor or even leads to system instability.…”

Section: Ceffect Of Parameter Mismatch On Mpccmentioning

confidence: 99%

“…Then, the perturbations are compensated into the controller and the predictive model is updated in real time. Thus, the robustness and stability of the system are improved [13][14][15].…”

Section: Proposed Mfpcc Algorithmmentioning

confidence: 99%

“…A fuzzy KF is designed to enhance the anti-interference ability of GCI under timevarying noise environment [14]. In [15], an MFPCC applied to control a permanent magnet synchronous motor is designed to estimate the disturbances by using an extended KF. The system control structure is further simplified.…”

Section: Introductionmentioning

confidence: 99%

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Model-Free Predictive Current Control of Three-Level Grid-Connected Inverters With LCL Filters Based on Kalman Filter

et al. 2023

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With the high-speed development of digital signal processors, model predictive current control (MPCC) has been widely used in power converters. However, the control robustness of MPCC is poor because of its strong dependence on the model parameters. In this paper, an ultra-local model-free predictive current control (MFPCC) for three-level grid-connected inverters (GCI) with LCL filters is proposed. Based on ultralocal theory, a third-order ultra-local model of the GCI with LCL filters is constructed. Then, a Kalman filter (KF) is introduced to estimate the three perturbations. Moreover, the perturbations are compensated to the predictive current model, thus reducing the number of model parameters involved in the predictive control. Finally, simulation results show that the proposed MFPCC improves the tracking performance of the grid current and strengthens the dynamic response capabilities under the parameter mismatch. Furthermore, the output power quality becomes higher, and the system robustness is also enhanced under noisy environment.

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Section: Ceffect Of Parameter Mismatch On Mpccmentioning

confidence: 99%

“…Then, the perturbations are compensated into the controller and the predictive model is updated in real time. Thus, the robustness and stability of the system are improved [13][14][15].…”

Section: Proposed Mfpcc Algorithmmentioning

confidence: 99%