A Model Predictive Current Controller With Improved Robustness Against Measurement Noise and Plant Model Variations

Pérez-Estévez, Diego; Doval-Gandoy, Jesús

doi:10.1109/ojia.2021.3074502

Cited by 11 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The impact of the measurement noise on MPC is addressed in very few studies [48,49]. Given the fact that the MPCC performance largely depends on the quality of the measured current and speed signals, this issue should not be overlooked and some kind of filtering with delay compensation might be necessary to achieve satisfactory performance.…”

Section: Practical Considerations and Challengesmentioning

confidence: 99%

Model Predictive Current Control of an Induction Motor Considering Iron Core Losses and Saturation

Bašić,

Vukadinović,

Grgić

2023

Processes

View full text Add to dashboard Cite

The paper considers the model predictive current control (MPCC) of an induction motor (IM) drive and evaluates five IM models of different complexities—from conventional to magnetic saturation, iron losses, and stray-load losses—for the MPCC design. The validity of each considered IM model and the corresponding MPCC algorithm is evaluated by comparison of the following performance metrics: the total harmonic distortion of the stator current, the average switching frequency, the rotor flux magnitude error, the rotor flux angle error, and the product of the first two metrics. The metrics’ values are determined in wide ranges of the rotor speed (0.1–1 p.u.) and load torque (0–1 p.u.) through simulations performed in the MATLAB Simulink environment. The obtained results allow us to identify the IM model that offers the best tradeoff between the practicability and accuracy. Furthermore, a control effort penalization (CEP) is suggested to reduce the average switching frequency and, hence, the power converter losses. This involves constraining the simultaneous switching to a maximum of two branches of the three-phase power converter, as well as inclusion of the weighted switching penalization term in the cost function. Finally, the performance—both steady-state and dynamic—of the proposed MPCC system with CEP is compared with that of the analogous field-oriented controlled (FOC) IM drive. The inverter switching frequency is reduced more than twice by including the frequency-dependent iron-loss resistance in the MPCC. It is additionally reduced by implementing the proposed CEP strategy without sacrificing many other performance metrics, thus achieving a performance comparable to the FOC IM drive.

show abstract

Section: Practical Considerations and Challengesmentioning

confidence: 99%

Model Predictive Current Control of an Induction Motor Considering Iron Core Losses and Saturation

Bašić,

Vukadinović,

Grgić

2023

Processes

View full text Add to dashboard Cite

show abstract

“…Ref. [11] uses a state observer to reduce current distortion without increasing the amount of computation, and retains sufficient response speed in noisy environments. Ref.…”

Section: Introductionmentioning

confidence: 99%

Robust Predictive Current Control of PMSG Wind Turbines with Sensor Noise Suppression

Li,

Babayomi,

Zhang

et al. 2023

Energies

View full text Add to dashboard Cite

Model predictive control (MPC) is an efficient and multi-functional control scheme for synchronous permanent magnet generators (PMSGs). However, the effective management of traditional MPC depends on precise system models. Multiple uncertainties of permanent magnet flux, motor inductance, filter inductance and parameter measurement noise will limit MPC’s performance. The conventional linear extended state observer (ESO) can perform robust predictive control of the ultralocal model of the PMSG system to cope with parameter mismatches. However, the ESO is limited in balancing disturbance rejection with measurement noise attenuation. Since the amplification of high-frequency noise pollution can lead to both poor control performance and system instability, this challenge is of significant importance. To solve the problem, a new hybrid parallel cascaded ESO (PCESO) model-free predictive control framework is proposed using the three-level neutral-point-clamped (NPC) power electronic converter, on both the machine side and grid side. Analytical discussions of the time and frequency domain characteristics of the PCESO demonstrate its superior characteristics over the ESO. The proposed method can effectively balance parameter mismatch, disturbance rejection and high-frequency noise suppression. Finally, the effectiveness of the proposed method, under uncertainties of parameter mismatches, measurement noise and permanent magnet flux, is verified through real-time hardware-in-the-loop tests on a back-to-back grid-tied PMSG interfaced with an NPC power converter.

show abstract

“…In this sense, concerning model-based control techniques, there are numerous options with their advantages and disadvantages. Among them is the MPC with finite set control, is one of the most widely used [7][8][9][10][11]. This technique employs a cost function to select an optimal vector from the 64 available vectors, for a six-phase IM, and apply it during the sampling period.…”

Section: Introductionmentioning

confidence: 99%

Algorithm for Implementation of Optimal Vector Combinations in Model Predictive Current Control of Six-Phase Induction Machines

et al. 2021

View full text Add to dashboard Cite

The development of new control techniques for multiphase induction machines (IMs) has become a point of great interest to exploit the advantages of these machines compared to three-phase topology, for example, the reduced phase currents and lower harmonic contents. One of the most analyzed techniques is the model-based predictive current control (MPC) with a finite control set. This technique presents high x–y currents because of the application of one switching state throughout the whole sampling period. Nevertheless, it is one of the most used due to its excellent dynamic response. To overcome the aforementioned drawbacks, new techniques called virtual vectors have been developed, but although there are several articles with experimental results, the algorithm for implementing the technique has not been appropriately described. This document provides a clear and detailed explanation for algorithm implementation of virtual vectors through two proposed variants VV4 and VV11, in a six-phase machine drive. The first entails lower computational cost and the second lower loss in the x–y plane. According to performance indicators such as the total harmonic distortion and the mean square error for both case studies, experimental tests were evaluated to determine the implementation’s behaviour.

show abstract

A Model Predictive Current Controller With Improved Robustness Against Measurement Noise and Plant Model Variations

Cited by 11 publications

References 45 publications

Model Predictive Current Control of an Induction Motor Considering Iron Core Losses and Saturation

Model Predictive Current Control of an Induction Motor Considering Iron Core Losses and Saturation

Robust Predictive Current Control of PMSG Wind Turbines with Sensor Noise Suppression

Algorithm for Implementation of Optimal Vector Combinations in Model Predictive Current Control of Six-Phase Induction Machines

Contact Info

Product

Resources

About