Enhancing performance of BLDC: An improved fast terminal slide mode control for BLDC motor speed control

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Authors: Omar Muhammed Neda
Cited by: 6
References: 38

Abstract

ABSTRACT The brushless DC (BLDC) motor offers higher efficiency, reliability, and dynamic performance compared to conventional brushed DC motors, making it suitable for a wide range of industrial and automation applications. However, achieving precise and robust speed control under varying loads and reference speeds remains a significant challenge. This work aims to enhance BLDC motor speed regulation by developing an improved fast terminal slide mode control (IFTSMC) strategy. The proposed controller is designed to ensure fast convergence, reduced chattering, and improved tracking accuracy. The performance of the IFTSMC is evaluated in MATLAB/Simulink and benchmarked against proportional–integral (PI), conventional sliding mode control (SMC), terminal SMC (TSMC), and fast terminal SMC (FTSMC) under four different operating cases involving changes in load and reference speed. Quantitative results show that in case 1, IFTSMC achieves a fast rise time of 0.032 s and a settling time of 0.040 s, while limiting overshoot to only 0.08%, compared to 47.39% for PI and 8.76% for SMC. In case 2, IFTSMC produces an extremely small overshoot of 0.006% and a low steady-state error (SSE) of 0.520, outclassing the PI controller (SSE = 3.609) and SMC (SSE = 5.31). For case 3, IFTSMC limits SSE to 0.0095, significantly lower than PI (3.755) and SMC (6.415). In case 4, it maintains strong robustness with a settling time of 0.090 s and SSE of 0.130, while PI and SMC suffer from high SSE values of 6.795 and 6.881, respectively. Overall, the simulation results confirm that the proposed IFTSMC method provides superior dynamic response, minimal overshoot, and high accuracy across all test scenarios. Its robustness and precision make it a highly effective solution for BLDC motor speed control in modern industrial applications requiring optimal performance under variable operating conditions.

Fields of Study & Subjects

Sensorless Control of Electric Motors Iterative Learning Control Systems Adaptive Control of Nonlinear Systems

Publication details

Journal / source
Franklin Open
Publisher
Elsevier BV
Volume
14
Pages
100510–100510
Publication date
21 January 2026
Language
EN