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

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

Govindharaj I  1 , Dinesh Kumar K  1 , Balamurugan S  2 , Yazhinian S  3 , Anandh R  4 , Rampriya R  5 , Karthick G  6 , Michael G  7
Affiliations
Review

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

Govindharaj I et al. MethodsX. .

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. •ANFIS integration for improved control: 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.•Increased stability and robustness: 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.•Superior performance Validated: 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.

Keywords: Adaptive Neuro-Fuzzy Inference System; Induction motor drive; Integration of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Model Reference Adaptive System (MRAS); Load variations; Model reference adaptive system; Rotor speed estimation; Sensorless induction motor; Speed control.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image, graphical abstract
Graphical abstract
Fig. 1:
Fig. 1
Proposed workflow model.
Fig. 2:
Fig. 2
Configuration control of IM with MRAS module for speed estimation.
Fig. 3
Fig. 3
Structure of ANFIS - MRAS system.
Fig. 4:
Fig. 4
Experimental setup.
Fig. 5:
Fig. 5
Rule viewer.
Fig. 6:
Fig. 6
Membership functions of ANFIS system inputs designed to estimate extra torque.
Fig. 7:
Fig. 7
Training data in the ANFIS tool.
Fig. 8:
Fig. 8
New trained data using ANFIS tuning tool.
Fig. 9:
Fig. 9
ANFIS model rule base structure.
Fig. 10:
Fig. 10
ANFIS surface viewer.
Fig. 11:
Fig. 11
Performance under test1 with PI based MRAS.
Fig. 12:
Fig. 12
Performance under test-1 with ANFIS based MRAS.
Fig. 13:
Fig. 13
Performance under test2 with PI based MRAS.
Fig. 14:
Fig. 14
Performance under test2 with ANFIS based MRAS.
Fig. 15:
Fig. 15
Performance under test3 with PI based MRAS.
Fig. 16:
Fig. 16
Performance under test3 with ANFIS based MRAS.
Fig. 17:
Fig. 17
Performance under test 4 with PI based MRAS.
Fig. 18:
Fig. 18
Performance under test4 with ANFIS based MRAS.
Fig. 19:
Fig. 19
Performance of speed under test 5 with proportional integral based MRAS.
Fig. 20:
Fig. 20
Performance of torque under test 5 with PI based MRAS.
Fig. 21:
Fig. 21
Performance of current under test5 with PI based MRAS.
Fig. 22:
Fig. 22
Performance of flux under test5 with PI based MRAS.
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