Enhanced Robust Control of Induction Motor Using Combined Optimal Model Predictive Control With Super-Twisting Algorithm
This paper presents a novel strategy for induction motor control that combines Optimal Model Predictive Control (OMPC) with the Super-Twisting Algorithm (STA) to enhance the performance of field-oriented control (IFOC) strategy under disturbances and uncertainties. OMPC is exploited for its capabili...
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| Veröffentlicht in: | Advances in Electrical and Computer Engineering Jg. 25; H. 2; S. 37 - 48 |
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| Sprache: | Englisch |
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Suceava
Stefan cel Mare University of Suceava
01.06.2025
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| ISSN: | 1582-7445, 1844-7600 |
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| Abstract | This paper presents a novel strategy for induction motor control that combines Optimal Model Predictive Control (OMPC) with the Super-Twisting Algorithm (STA) to enhance the performance of field-oriented control (IFOC) strategy under disturbances and uncertainties. OMPC is exploited for its capability to optimally handle multivariable systems with constraints, but suffers from high computational demands, sensitivity to model inaccuracies, and limited robustness against disturbances. To address these limitations, the proposed approach integrates STA, a second-order sliding mode technique, which provides robustness when subjected to model mismatch and disturbances, while reducing the chattering effect typically associated with classical sliding mode control. By incorporating OMPC with STA into the speed and currents loops of the IFOC technique, the system gains enhanced robustness and disturbance rejection capabilities, without increasing computational cost making it viable for real-time applications in complex control scenarios. This synergetic approach ensures stable and efficient performance in the face of internal variations (like parameters variation) and external perturbations (variable references and load torque). Simulation results demonstrate that the combined OMPC-STA strategy outperforms traditional PI and SMC methods in terms of tracking accuracy, robustness, providing a more reliable control solution for high-performance drives. Index Terms--induction motor, predictive control, robust control, sliding mode control, variable speed drives. |
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| AbstractList | This paper presents a novel strategy for induction motor control that combines Optimal Model Predictive Control (OMPC) with the Super-Twisting Algorithm (STA) to enhance the performance of field-oriented control (IFOC) strategy under disturbances and uncertainties. OMPC is exploited for its capability to optimally handle multivariable systems with constraints, but suffers from high computational demands, sensitivity to model inaccuracies, and limited robustness against disturbances. To address these limitations, the proposed approach integrates STA, a second-order sliding mode technique, which provides robustness when subjected to model mismatch and disturbances, while reducing the chattering effect typically associated with classical sliding mode control. By incorporating OMPC with STA into the speed and currents loops of the IFOC technique, the system gains enhanced robustness and disturbance rejection capabilities, without increasing computational cost making it viable for real-time applications in complex control scenarios. This synergetic approach ensures stable and efficient performance in the face of internal variations (like parameters variation) and external perturbations (variable references and load torque). Simulation results demonstrate that the combined OMPC-STA strategy outperforms traditional PI and SMC methods in terms of tracking accuracy, robustness, providing a more reliable control solution for high-performance drives. This paper presents a novel strategy for induction motor control that combines Optimal Model Predictive Control (OMPC) with the Super-Twisting Algorithm (STA) to enhance the performance of field-oriented control (IFOC) strategy under disturbances and uncertainties. OMPC is exploited for its capability to optimally handle multivariable systems with constraints, but suffers from high computational demands, sensitivity to model inaccuracies, and limited robustness against disturbances. To address these limitations, the proposed approach integrates STA, a second-order sliding mode technique, which provides robustness when subjected to model mismatch and disturbances, while reducing the chattering effect typically associated with classical sliding mode control. By incorporating OMPC with STA into the speed and currents loops of the IFOC technique, the system gains enhanced robustness and disturbance rejection capabilities, without increasing computational cost making it viable for real-time applications in complex control scenarios. This synergetic approach ensures stable and efficient performance in the face of internal variations (like parameters variation) and external perturbations (variable references and load torque). Simulation results demonstrate that the combined OMPC-STA strategy outperforms traditional PI and SMC methods in terms of tracking accuracy, robustness, providing a more reliable control solution for high-performance drives. Index Terms--induction motor, predictive control, robust control, sliding mode control, variable speed drives. |
| Audience | Academic |
| Author | REZGUI, S.-E. NEMOUCHI, B. |
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| SubjectTerms | Algorithms Comparative analysis Computing costs Control Controllers Disturbances Induction electric motors induction motor Induction motors Optimization Optimization techniques Performance enhancement Predictive control Process control Real time Robust control Sliding mode control Technology application Twisting variable speed drives Variables |
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