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无传感器矢量控制感应电动机驱动器：通过集成增强型模型参考自适应系统的自适应神经模糊推理系统提升性能。

Sensorless vector-controlled induction motor drives: Boosting performance with Adaptive Neuro-Fuzzy Inference System integrated augmented Model Reference Adaptive System.

作者信息

I Govindharaj, K Dinesh Kumar, S Balamurugan, S Yazhinian, R Anandh, R Rampriya, G Karthick, G Michael

机构信息

Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu 600062, India.

School of Computer Science and IT, JAIN (Deemed-to-be University), Karnataka 560069, India.

出版信息

MethodsX. 2024 Oct 5;13:102992. doi: 10.1016/j.mex.2024.102992. eCollection 2024 Dec.

DOI:10.1016/j.mex.2024.102992
PMID:39676841
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11639364/
Abstract

The Model Reference Adaptive System (MRAS) is effective for speed control in sensorless Induction Motor (IM) drives, particularly at zero and very low speeds. This study enhances MRAS's resilience and dynamic performance by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller into sensorless vector-controlled IM drives. The research addresses challenges related to parameter uncertainties, load variations, and external disturbances through the combination of MRAS and ANFIS. The ANFIS controller enhances dynamic performance by adjusting its parameters based on the error between estimated and measured rotor speeds, which improves reference speed tracking and ensures smoother drive operation. This integration of ANFIS with MRAS reduces the sensitivity of the sensorless control system to parameter variations, such as changes in motor parameters or load torque, thereby enhancing system stability. The primary goal is to ma-intain stability and mitigate the impact of parameter variations on the sensorless control system. The proposed MRAS-ANFIS scheme was evaluated using MATLAB and compared with existing systems. Results show that the ANFIS-enhanced MRAS delivers superior dynamic performance and robustness, proving to be an effective solution for applications demanding precise speed control and high reliability. • integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) with MRAS enhances the dynamic performance and resilience of sensorless Induction Motor (IM) drives, particularly at zero and very low speeds.• The ANFIS controller adapts to parameter uncertainties, load variations, and disturbances, improving speed tracking and reducing sensitivity to motor parameter changes, thus enhancing system stability.• MATLAB simulations show that the ANFIS-enhanced MRAS outperforms existing systems, offering superior dynamic performance and robustness, making it ideal for precise speed control applications

摘要

模型参考自适应系统（MRAS）在无传感器感应电机（IM）驱动的速度控制中非常有效，尤其是在零速和极低速度时。本研究通过将自适应神经模糊推理系统（ANFIS）控制器集成到无传感器矢量控制的感应电机驱动中，提高了MRAS的弹性和动态性能。该研究通过MRAS和ANFIS的结合，解决了与参数不确定性、负载变化和外部干扰相关的挑战。ANFIS控制器通过根据估计转子速度和测量转子速度之间的误差调整其参数来提高动态性能，这改善了参考速度跟踪并确保了更平稳的驱动运行。ANFIS与MRAS的这种集成降低了无传感器控制系统对参数变化（如电机参数或负载转矩的变化）的敏感性，从而提高了系统稳定性。主要目标是保持稳定性并减轻参数变化对无传感器控制系统的影响。使用MATLAB对提出的MRAS-ANFIS方案进行了评估，并与现有系统进行了比较。结果表明，ANFIS增强的MRAS具有卓越的动态性能和鲁棒性，被证明是对要求精确速度控制和高可靠性的应用的有效解决方案。

• 将自适应神经模糊推理系统（ANFIS）与MRAS集成可提高无传感器感应电机（IM）驱动的动态性能和弹性，尤其是在零速和极低速度时。

• ANFIS控制器可适应参数不确定性、负载变化和干扰，改善速度跟踪并降低对电机参数变化的敏感性，从而提高系统稳定性。

• MATLAB仿真表明，ANFIS增强的MRAS优于现有系统，具有卓越的动态性能和鲁棒性，使其成为精确速度控制应用的理想选择

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