Current Distortion Rejection in PMSM Drives Using an Adaptive Super-Twisting Algorithm

Memije, D.; Rivas, Jaime José Rodríguez; Carranza, O.; González, R.; Rodarte, F. E

doi:10.1109/tec.2021.3131967

Cited by 15 publications

(2 citation statements)

References 28 publications

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“…Some disturbance rejection techniques for the magnetic bearing systems were discussed in [4,17,25,27]. The perturbations of the motor system can be rejected by an adaptive super-twisting control method [5]. In the best of the author's knowledge, the numerical paper discussed about the disturbance or vibration suppression techniques for the SSBM is still limited.…”

Section: Introductionmentioning

confidence: 99%

Eccentricity Reduction of Slotless Self- Bearing Motor System Based on Adaptive Neural Network and Sliding Mode Control

Giap

Nguyen

Pham

et al. 2023

Preprint

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This paper presents the effectiveness of a hybrid control method for the slotless self-bearing motor (SSBM), which combined by an improved adaptive radial basic function neural network (ARBFNN) and adaptive improved reaching law (AIRL) sliding-mode control (SMC) with the aim of softening the eccentricity of the rotor of SSBM. The source of eccentricity is considered from the disturbances and uncertainties and the self-vibration of the rotor. First, the mathematical model of the bearing motor system with fully embedded sources of the perturbations is represented. Second, the proposed control algorithms for SSBM system with the proof of stability via the Lyapunov theorem are given. Third, the illustrative examples of simulation and experimental studies are given to shown the effectiveness and power of the proposed control algorithms. The outcomes of the proposed control algorithms are small overshoots, small settling-times, and stable steady-states. The tested disturbances in the simulation study were mostly rejected by an ARBFNN. The performance of proposed controller with ARBFNN in experiment is better the case of controller without ARBFNN.

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Section: Introductionmentioning

confidence: 99%

Eccentricity Reduction of Slotless Self- Bearing Motor System Based on Adaptive Neural Network and Sliding Mode Control

Giap

Nguyen

Pham

et al. 2023

Preprint

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“…Controllers are designed to provide a fast response with minimum overshoot, together with the ability to reject disturbances (robustness) [15][16][17][18]. In this context, choosing appropriate control and tuning methods enhances device performance, reduces implementation costs, and allows for the downsizing of components such as filters [19][20][21][22]. Finding the optimal tuning (regardless of the selected control) poses challenges for control engineers when it is necessary to control several variables under various conditions.…”

Section: Introductionmentioning

confidence: 99%

Optimal Sliding-Mode Control of Semi-Bridgeless Boost Converters Considering Power Factor Corrections

Ortiz-Castrillón,

Saldarriaga-Zuluaga,

Muñoz-Galeano

et al. 2023

Energies

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Sliding-mode control (SMC) is a robust technique used in power electronics (PE) for controlling the behavior of power converters. This paper presents simulations and experimental results of an optimal SMC strategy applied to Semi-Bridgeless Boost Converters (SBBC), which includes Power Factor Correction (PFC). As the main contribution, the optimal coefficients of the SMC strategy are obtained using two metaheuristic approaches, namely the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The main objective is to obtain the sliding coefficients that ensure the best converter response in terms of the input current and output voltage, both during start-up and under disturbances (including changes in load, source, and references). The fitness function to be minimized includes two coefficients, namely the Integrative Absolute Error (IAE) and the Integral Time Absolute Error (ITAE), for both the input current and output voltage. These coefficients measure the converter’s effort to follow the control references. The IAE penalizes errors during start-up, whereas the ITAE penalizes errors in the steady state. The tests carried out demonstrated the effectiveness of the GA and PSO techniques in the optimization process; nonetheless, the GA outperformed the PSO approach, providing sliding coefficients that allowed for a reduction in the input current overshoot during start-up of up to 24.15% and a reduction in the setting time of the output voltage of up to 99%. The experimental results were very similar when tuning with the GA and PSO techniques; nevertheless, tuning with the GA technique produced a better response in the face of disturbances compared to the PSO technique.

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Research on Fractional-Order Sliding Mode PMSM Speed Regulation Based on Load Observer

Zhu,

Cao,

et al. 2024

J. Electr. Eng. Technol.

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Current Distortion Rejection in PMSM Drives Using an Adaptive Super-Twisting Algorithm

Cited by 15 publications

References 28 publications

Eccentricity Reduction of Slotless Self- Bearing Motor System Based on Adaptive Neural Network and Sliding Mode Control

Eccentricity Reduction of Slotless Self- Bearing Motor System Based on Adaptive Neural Network and Sliding Mode Control

Optimal Sliding-Mode Control of Semi-Bridgeless Boost Converters Considering Power Factor Corrections

Research on Fractional-Order Sliding Mode PMSM Speed Regulation Based on Load Observer

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