A Non-Integer High-Order Sliding Mode Control of Induction Motor with Machine Learning-Based Speed Observeropen access
- Authors
- Sami, Irfan; Ullah, Shafaat; Ullah, Shafqat; Bukhari, Syed Sabir Hussain; Ahmed, Naseer; Salman, Muhammad; Ro, Jong-Suk
- Issue Date
- Jun-2023
- Publisher
- MDPI
- Keywords
- sliding mode control; induction motor; observer; artificial intelligence
- Citation
- MACHINES, v.11, no.6
- Journal Title
- MACHINES
- Volume
- 11
- Number
- 6
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67466
- DOI
- 10.3390/machines11060584
- ISSN
- 2075-1702
2075-1702
- Abstract
- The induction motor (IM) drives are prone to various uncertainties, disturbances, and non-linear dynamics. A high-performance control system is essential in the outer loop to guarantee the accurate convergence of speed and torque to the required value. Super-twisting sliding mode control (ST-SMC) and fractional-order calculus have been widely used to enhance the sliding mode control (SMC) performance for IM drives. This paper combines the ST-SMC and fractional-order calculus attributes to propose a novel super-twisting fractional-order sliding mode control (ST-FOSMC) for the outer loop speed control of the model predictive torque control (MPTC)-based IM drive system. The MPTC of the IM drive requires some additional sensors for speed control. This paper also presents a novel machine learning-based Gaussian Process Regression (GPR) framework to estimate the speed of IM. The GPR model is trained using the voltage and current dataset obtained from the simulation of a three-phase MPTC based IM drive system. The performance of the GPR-based ST-FOSMC MPTC drive system is evaluated using various test cases, namely (a) electric fault incorporation, (b) parameter perturbation, and (c) load torque variations in Matlab/Simulink environment. The stability of ST-FOSMC is validated using a fractional-order Lyapunov function. The proposed control and estimation strategy provides effective and improved performance with minimal error compared to the conventional proportional integral (PI) and SMC strategies.
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