Data- and Model-Driven Adaptive Integral Terminal Sliding Mode Control for PMSM Speed Control

Yifu Ma; Ying Zhou; Shuo Zhang; Yuelin Dong; Xudong Zhang

doi:10.1109/JESTPE.2026.3651542

Data- and Model-Driven Adaptive Integral Terminal Sliding Mode Control for PMSM Speed Control

Yifu Ma^*
, Ying Zhou
, Shuo Zhang
, Yuelin Dong
, Xudong Zhang

^*Corresponding author for this work

School of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Simultaneously achieving minimal overshoot, fast response, and low oscillations under various operating conditions remains a major challenge in the speed control of permanent magnet synchronous motors (PMSMs). To address this, a data- and model-driven adaptive integral terminal sliding mode control (AITSMC) method is proposed, which uses a structurally adaptive sliding surface to achieve simultaneous optimization. Furthermore, a radial basis function neural network (RBFNN) is employed to adjust the AITSMC parameters online. The RBFNN model is trained offline to map load torque and target speed to optimal AITSMC parameters. Optimal datasets under varying operating conditions are obtained using an online automatic parameter calibration method. System stability is verified using Lyapunov analysis. Experimental results demonstrate the effectiveness and feasibility of the RBFNN-AITSMC approach.

Original language	English
Journal	IEEE Journal of Emerging and Selected Topics in Power Electronics
DOIs	https://doi.org/10.1109/JESTPE.2026.3651542
Publication status	Accepted/In press - 2026

Keywords

adaptive control
integral terminal sliding mode control
permanent magnet synchronous motor
RBF neural network

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10.1109/JESTPE.2026.3651542

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@article{b38405faff3748acb67bd3a3b9e39446,

title = "Data- and Model-Driven Adaptive Integral Terminal Sliding Mode Control for PMSM Speed Control",

abstract = "Simultaneously achieving minimal overshoot, fast response, and low oscillations under various operating conditions remains a major challenge in the speed control of permanent magnet synchronous motors (PMSMs). To address this, a data- and model-driven adaptive integral terminal sliding mode control (AITSMC) method is proposed, which uses a structurally adaptive sliding surface to achieve simultaneous optimization. Furthermore, a radial basis function neural network (RBFNN) is employed to adjust the AITSMC parameters online. The RBFNN model is trained offline to map load torque and target speed to optimal AITSMC parameters. Optimal datasets under varying operating conditions are obtained using an online automatic parameter calibration method. System stability is verified using Lyapunov analysis. Experimental results demonstrate the effectiveness and feasibility of the RBFNN-AITSMC approach.",

keywords = "adaptive control, integral terminal sliding mode control, permanent magnet synchronous motor, RBF neural network",

author = "Yifu Ma and Ying Zhou and Shuo Zhang and Yuelin Dong and Xudong Zhang",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2026",

doi = "10.1109/JESTPE.2026.3651542",

language = "English",

journal = "IEEE Journal of Emerging and Selected Topics in Power Electronics",

issn = "2168-6777",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Data- and Model-Driven Adaptive Integral Terminal Sliding Mode Control for PMSM Speed Control

AU - Ma, Yifu

AU - Zhou, Ying

AU - Zhang, Shuo

AU - Dong, Yuelin

AU - Zhang, Xudong

PY - 2026

Y1 - 2026

N2 - Simultaneously achieving minimal overshoot, fast response, and low oscillations under various operating conditions remains a major challenge in the speed control of permanent magnet synchronous motors (PMSMs). To address this, a data- and model-driven adaptive integral terminal sliding mode control (AITSMC) method is proposed, which uses a structurally adaptive sliding surface to achieve simultaneous optimization. Furthermore, a radial basis function neural network (RBFNN) is employed to adjust the AITSMC parameters online. The RBFNN model is trained offline to map load torque and target speed to optimal AITSMC parameters. Optimal datasets under varying operating conditions are obtained using an online automatic parameter calibration method. System stability is verified using Lyapunov analysis. Experimental results demonstrate the effectiveness and feasibility of the RBFNN-AITSMC approach.

AB - Simultaneously achieving minimal overshoot, fast response, and low oscillations under various operating conditions remains a major challenge in the speed control of permanent magnet synchronous motors (PMSMs). To address this, a data- and model-driven adaptive integral terminal sliding mode control (AITSMC) method is proposed, which uses a structurally adaptive sliding surface to achieve simultaneous optimization. Furthermore, a radial basis function neural network (RBFNN) is employed to adjust the AITSMC parameters online. The RBFNN model is trained offline to map load torque and target speed to optimal AITSMC parameters. Optimal datasets under varying operating conditions are obtained using an online automatic parameter calibration method. System stability is verified using Lyapunov analysis. Experimental results demonstrate the effectiveness and feasibility of the RBFNN-AITSMC approach.

KW - adaptive control

KW - integral terminal sliding mode control

KW - permanent magnet synchronous motor

KW - RBF neural network

UR - https://www.scopus.com/pages/publications/105027342994

U2 - 10.1109/JESTPE.2026.3651542

DO - 10.1109/JESTPE.2026.3651542

M3 - Article

AN - SCOPUS:105027342994

SN - 2168-6777

JO - IEEE Journal of Emerging and Selected Topics in Power Electronics

JF - IEEE Journal of Emerging and Selected Topics in Power Electronics

ER -

Data- and Model-Driven Adaptive Integral Terminal Sliding Mode Control for PMSM Speed Control

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Keywords

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