Optimization of Fuzzy Controller for Predictive Current Control of Induction Machine

Varga, Toni; Benšić, Tin; Barukčić, Marinko; Štil, Vedrana Jerković

doi:10.3390/electronics11101553

Cited by 6 publications

(5 citation statements)

References 49 publications

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“…The works cited in [22,32,35,36,38] have highlighted the influence of this variation and have calculated the deviation between the estimated values (C em , Φ s ) and their real values as a function of the real resistance R r s of the machine and the resistance R s used in the flux and torque estimation. These deviations are given in the equations below:…”

Section: R S Impact On Dtcmentioning

confidence: 99%

“…The fuzzification of the input and output variables of the fuzzy estimator is illustrated in Figure 19; each of the three linguistic variables is represented by five fuzzy subsets (NL ≡ Negative Large, NS ≡ Negative Small, ZE ≡ Null, PS ≡ Positive Small, and PL ≡ Positive Large) [20,23,[37][38][39]. The fuzzification of the input and output variables of the fuzzy estimator is illustrated in Figure 19; each of the three linguistic variables is represented by five fuzzy subsets (NL ≡ Negative Large, NS ≡ Negative Small, ZE ≡ Null, PS ≡ Positive Small, and PL ≡ Positive Large) [20,23,[37][38][39]. The fuzzification of the input and output variables of the fuzzy estimator is illustrated in Figure 19; each of the three linguistic variables is represented by five fuzzy subsets (NL ≡ Negative Large, NS ≡ Negative Small, ZE ≡ Null, PS ≡ Positive Small, and PL ≡ Positive Large) [20,23,[37][38][39].…”

Section: Dtc Improved By the Fuzzy Estimatormentioning

confidence: 99%

“…The fuzzification of the input and output variables of the fuzzy estimator is illustrated in Figure 19; each of the three linguistic variables is represented by five fuzzy subsets (NL ≡ Negative Large, NS ≡ Negative Small, ZE ≡ Null, PS ≡ Positive Small, and PL ≡ Positive Large) [20,23,[37][38][39]. The fuzzification of the input and output variables of the fuzzy estimator is illustrated in Figure 19; each of the three linguistic variables is represented by five fuzzy subsets (NL ≡ Negative Large, NS ≡ Negative Small, ZE ≡ Null, PS ≡ Positive Small, and PL ≡ Positive Large) [20,23,[37][38][39]. Defuzzification was also carried out using the center of gravity method combined with the sum-product inference method.…”

Section: Dtc Improved By the Fuzzy Estimatormentioning

confidence: 99%

See 2 more Smart Citations

Stability Analysis of a Wind Turbine Controlled by Direct Torque Control

Ikni,

Raducan

2024

Energies

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Increasingly, electricity network managers, through their grid codes, require renewable energy production systems to participate in system services, which includes requirements such as the stability of these production systems, the quality of the energy injected into the networks, the ability to withstand voltage dips, etc. To meet these requirements, the use of appropriate commands for the control of the production systems is necessary. Various control methods have been proposed, among which direct torque control (DTC) stands out. However, several studies have highlighted the impact of parametric variations on this control method. The contribution of the work presented in this article is the improvement of DTC when combined with a fuzzy estimate applied to a wind production system based on an asynchronous machine. Robustness tests were simulated to highlight the sensitivity of this control to variations in the stator resistance of asynchronous machines. To make this command robust and stable, a fuzzy estimator was used with this command. The simulation results demonstrated that this combination (DTC with a fuzzy estimator) makes the wind system more stable. To assess the effectiveness of the proposed solution, the root mean square error index was used.

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Section: R S Impact On Dtcmentioning

confidence: 99%

Section: Dtc Improved By the Fuzzy Estimatormentioning

confidence: 99%

“…The fuzzification of the input and output variables of the fuzzy estimator is illustrated in Figure 19; each of the three linguistic variables is represented by five fuzzy subsets (NL ≡ Negative Large, NS ≡ Negative Small, ZE ≡ Null, PS ≡ Positive Small, and PL ≡ Positive Large) [20,23,[37][38][39]. The fuzzification of the input and output variables of the fuzzy estimator is illustrated in Figure 19; each of the three linguistic variables is represented by five fuzzy subsets (NL ≡ Negative Large, NS ≡ Negative Small, ZE ≡ Null, PS ≡ Positive Small, and PL ≡ Positive Large) [20,23,[37][38][39]. Defuzzification was also carried out using the center of gravity method combined with the sum-product inference method.…”

Section: Dtc Improved By the Fuzzy Estimatormentioning

confidence: 99%

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Stability Analysis of a Wind Turbine Controlled by Direct Torque Control

Ikni,

Raducan

2024

Energies

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show abstract

“…MPC has the advantages of a simple and intuitive concept, good control performance, easy to deal with nonlinear constraints, etc. For motor control, it is known as model predictive torque control (MPTC) or model predictive current control (MPCC) [14][15][16][17]. Among them, MPTC has the advantage of high real-time response, but traditional MPTC considers both torque and flux variables at the same time, so it needs to add a weight coefficient into the cost function to balance the effect of flux and torque.…”

Section: Introductionmentioning

confidence: 99%

A Low Switching Frequency Model Predictive Control Method for an Induction Motor Fed by a 3-Level Inverter

Huang,

Jiang,

Zhang

et al. 2023

Electronics

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Traditional model predictive control (MPC) for the induction motor fed by a three-level inverter needs to explore 27 voltage vectors to obtain the optimal one, which leads to high switching frequency and requires too much computation. To solve this issue, a low switching frequency model predictive control with partition optimization is proposed. First, the reference voltage vector can be gained from the prediction model at the next time, and the space voltage vector plane is divided into 12 sectors for further vector choice. Furthermore, considering inverter constraints, the candidate voltage vectors are determined according to the sector location of the reference voltage vector. In this way, the candidate vectors can be reduced to 3 at most. Then, a boundary circle limit is designed to avoid unnecessary switch changes. If the reference voltage vector is within the boundary limit, the switches do not act, which can reduce the system switching frequency without introducing the extra weight coefficient into the cost function. These selected voltage vectors are substituted into the cost function to determine the optimal one. Finally, the neutral point voltage deviation is controlled by the positive and negative redundant small vectors to realize the multi-objective constraint without weighting coefficients. The simulation results show that the proposed control method can significantly reduce the switching frequency; at the same time, both the dynamic and steady performances can be maintained well, and the cost function has no weight coefficients.

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“…The last published article (in chronological order) [7] in the Special Issue deals with the optimal control of induction motors. In the article, the application of a fuzzy controller for the predictive current control of an induction motor was developed.…”

mentioning

confidence: 99%