Efficiency enhanced adaptive quasi-sliding mode controller for variable-speed induction motor drive

Recent advancements in automated manufacturing and processing industries necessitate fast-responding, efficient, and robust methods for controlling induction motor (IM) drives. Classical proportional-integral (PI) controllers provide optimal performance only at specific operating points and are sensitive to parameter variations. This work proposes an adaptive quasi-sliding mode controller (AQSMC), which utilizes a tangent (tanh) function as the switching function and demonstrates enhanced robustness and adaptability across a wider range of operating conditions. The AQSMC employs an adaptation law to estimate the dynamic disturbances, offering insensitivity to structured and unstructured uncertainties. Numerical simulations are carried out with the AQSMC that analytically deduces the optimum field flux ensuring efficient performance. A lookup table derived from the efficiency optimization algorithm (EOA) is incorporated to further streamline the computational requirements. To validate simulation results, a prototype was developed using a 1 HP induction motor, a DSP controller board with a TI C2000 Delfino MCU F28379D microcontroller, and an IGBT-based Inverter module. Simulations show a 6.3% efficiency improvement at half load and 300 rpm, while experimental analysis records a 3.9% improvement with the EOA, highlighting the potential for enhancing energy efficiency in various industrial applications.

Authors

Institutions

Publication Details

Journal
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering
Published
2025-02-12
DOI
https://doi.org/10.11591/ijpeds.v16.i1.pp151-161
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Efficiency enhanced adaptive quasi-sliding mode controller for variable-speed induction motor drive

Asha Elizabeth Daniel, S. Jacob
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering
Sensorless Control of Electric Motors
article

Efficiency enhanced adaptive quasi-sliding mode controller for variable-speed induction motor drive

Asha Elizabeth Daniel, S. Jacob
article en

Abstract

Recent advancements in automated manufacturing and processing industries necessitate fast-responding, efficient, and robust methods for controlling induction motor (IM) drives. Classical proportional-integral (PI) controllers provide optimal performance only at specific operating points and are sensitive to parameter variations. This work proposes an adaptive quasi-sliding mode controller (AQSMC), which utilizes a tangent (tanh) function as the switching function and demonstrates enhanced robustness and adaptability across a wider range of operating conditions. The AQSMC employs an adaptation law to estimate the dynamic disturbances, offering insensitivity to structured and unstructured uncertainties. Numerical simulations are carried out with the AQSMC that analytically deduces the optimum field flux ensuring efficient performance. A lookup table derived from the efficiency optimization algorithm (EOA) is incorporated to further streamline the computational requirements. To validate simulation results, a prototype was developed using a 1 HP induction motor, a DSP controller board with a TI C2000 Delfino MCU F28379D microcontroller, and an IGBT-based Inverter module. Simulations show a 6.3% efficiency improvement at half load and 300 rpm, while experimental analysis records a 3.9% improvement with the EOA, highlighting the potential for enhancing energy efficiency in various industrial applications.

International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer EngineeringVol. 16(1)
Cochin University of Science and Technology (IN), Universidad Modelo (MX)
Openalex Percentile: Top 98%
Sensorless Control of Electric Motors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.