Fuzzy-second order sliding mode control optimized by genetic algorithm applied in direct torque control of dual star induction motor

Boukhalfa, Ghoulemallah; Belkacem, Sebti; Chikhi, Abdesselem; Bouhentala, Moufid

doi:10.1007/s11771-022-5028-3

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…The sectors of the fluxes are divided into six sectors, each covering an angle π/3 and represented in a complex twodimensional plane. The location of the space vector of fluxes in the complex plane can be represented as follows [14,23]…”

Section: Dtc Strategymentioning

confidence: 99%

“…In our work, the performance index applied is ITSE. The optimized design of this regulator is done by a strategy consisting of a GA to precisely locate the global minimum using the 'Gatool' window under Matlab [23]. Figure 8 (Table 3), illustrates the gains synthesized by the conventional DTC, as well as the optimal optimizer gains by the GA algorithm.…”

Section: Genetic Algorithm Controllermentioning

confidence: 99%

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Tuning of Pi Speed Controller in Direct Torque Control Ofdual Star Induction Motor Based on Genetic Algorithms and Neuro-Fuzzy Schemes

BELAL,

FLITTI,

ZEGAI

2024

RRST-EE

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Thanks to the positive characteristics of the double stator machine (DSIM), its high reliability, and reduced rotor torque ripples, it has become among the most important multiphase machines included in industrial applications. This article aims to apply the two techniques of artificial intelligence represented by the adaptive neuro-fuzzy inference systems (ANFIS) and the genetic algorithm (GA) for direct torque control (DTC) of the DSIM to improve the performance of the machine. The ability to learn and the parallelism of operation characteristics made exploiting the GA to control the machine possible instead of using the proportional integral controller (PI). Fixed switching frequency obtained, given with the vector selection table and hysteresis, allowed the inclusion of the ANFIS technique in the DTC strategy. Two-level inverters are included to feed the DSIM. Several results prove that the two techniques applied, ANFIS and GA, improve the quality of the electromagnetic torque and flux and the dynamic responses of the DSIM.

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Section: Dtc Strategymentioning

confidence: 99%

Section: Genetic Algorithm Controllermentioning

confidence: 99%

Tuning of Pi Speed Controller in Direct Torque Control Ofdual Star Induction Motor Based on Genetic Algorithms and Neuro-Fuzzy Schemes

BELAL,

FLITTI,

ZEGAI

2024

RRST-EE

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“…The structure of the converter circuit system based on fuzzy PI control consists of a main circuit and the fuzzy PI controller, as shown in Figure 5. The fuzzy PI controller considers the output voltage as the controlled object, taking the error (e) between the output voltage and its ideal reference value and its error rate of change (𝑒 = 𝑑𝑒 𝑑𝑡 ⁄ ) as the two input quantities for the fuzzy controller [14,15]. First, the parameters are fuzzified by setting a unified fuzzy physical theory domain range to [−Em, Em].…”

Section: Design Of Fuzzy Pi Controllermentioning

confidence: 99%

Power Source Converter Based on a Variable-Domain Fuzzy PI Control

Chen,

Hu,

Wang

et al. 2024

Electronics

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The inadequacy of conventional control strategies for multi-phase interleaved parallel circuits in terms of adaptive adjustment makes it difficult to meet the power source design requirements for transient electromagnetic detection systems. This paper introduces a novel approach, the variable-domain fuzzy proportional–integral (PI) adaptive control strategy. This strategy dynamically adjusts the fuzzy domain in real time based on the input error magnitude, ensuring improved control effectiveness. By leveraging the benefits of both the functional scaling factor and the fuzzy reasoning scaling factor, we design scaling factors for the input and output domains to enhance control precision. The focus of this study is on a four-phase interleaved parallel converter, emphasizing the design of the variable-domain fuzzy PI control strategy for the voltage outer loop within the traditional dual closed-loop structure. An experimental prototype with a 220 Vac input, 380 V output, and a power rating of 1000 W is constructed. A comparative analysis between fuzzy control and variable-domain fuzzy PI control is conducted in the voltage outer loop of the dual closed-loop control. Results reveal that dual closed-loop control with variable-domain fuzzy PI control for the voltage outer loop significantly enhances system stability. The startup time to reach the steady state is reduced to 0.632 s, with an overshoot of 28.8 V. Transitioning from 25% load to full load takes only 0.096 s, resulting in a minimal drop of 21.4 V and an overshoot of 13.4 V. Similarly, switching from full load to 25% load in 0.167 s exhibits an overshoot of only 19.6 V. The adaptive regulation capability of the converter is markedly improved, showcasing smaller overshoots and higher-level controlling effectiveness.

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“…Dual star induction motor has noticeable potential because of their reliability and ability to operate in degraded conditions [4,5]. However, despite these advantages, their control remains rather complicated compared to the DC machines because the mathematical model is non-linear and intrinsically coupled [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…The simpler and effective solutions that exist nowadays in the majority of engineering control applications employ proportional integral derivative (PID) in the regulation loop [13]. On the other hand, lots of nonlinear control laws have been proposed including sliding mode control (SMC) [14,15], synergetic control [16][17][18], predictive control [19], backstepping control [20,21], and high-order SMC techniques [5]. In addition, the response and the speed dynamic of a system can be improved using intelligent techniques, such as fuzzy logic and neural networks [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Optimal Fractional Order Proportional Integral Controller for Dual Star Induction Motor Based on Particle Swarm Optimization Algorithm

Terfia,

Rezgui,

Mendaci

et al. 2023

JESA

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The purpose of this paper is to improve the performance of the conventional direct torque control (DTC) method of a dual star induction motor (DSIM) by enhancing speed control and reducing the ripples of electromagnetic torque and stator current. To achieve this, we propose a new optimal tuned controller based on the combination of a fractional order proportional integral controller (FOPI) and particle swarm optimization (PSO) algorithm. The aim of this high-performance controller is to reduce the rise time, settling time, steadystate error, and effects of load disturbances in the speed response of the DSIM, as well as minimize oscillations in the torque and stator currents, particularly at low speeds. This strategy named DTC-FOPI-PSO will be investigated, and its performances will be compared with the traditional DTC strategy based on the classical PI controller. Simulation tests using MATLAB/Simulink software are conducted under different operating conditions to demonstrate that the proposed DTC-FOPI-PSO strategy has a direct impact on improving speed dynamic, reducing torque fluctuations, minimizing steady-state error and provides excellent performance for load variation and reference speed inversion.

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Fuzzy-second order sliding mode control optimized by genetic algorithm applied in direct torque control of dual star induction motor

Cited by 9 publications

References 32 publications

Tuning of Pi Speed Controller in Direct Torque Control Ofdual Star Induction Motor Based on Genetic Algorithms and Neuro-Fuzzy Schemes

Tuning of Pi Speed Controller in Direct Torque Control Ofdual Star Induction Motor Based on Genetic Algorithms and Neuro-Fuzzy Schemes

Power Source Converter Based on a Variable-Domain Fuzzy PI Control

Optimal Fractional Order Proportional Integral Controller for Dual Star Induction Motor Based on Particle Swarm Optimization Algorithm

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