Research Article
Fault-Tolerant Control Using Fuzzy Logic for Induction Motor Drives
Rodrigue Armel Patrick Okemba*
,
Rostand Martialy Davy Loemba Souamy,
Haroun Abba Labane,
Amos Omboua Eyandzi
Issue:
Volume 15, Issue 4, August 2026
Pages:
65-78
Received:
1 July 2026
Accepted:
14 July 2026
Published:
17 August 2026
Abstract: Asynchronous motor drives are extensively used in industrial applications due to their robustness, efficiency, and relatively low cost. However, their performance can be significantly degraded by sensor failures, parameter variations, or power supply disturbances, which compromise reliability and voltage stability. The objective of this research is to propose a fault?tolerant control strategy that enhances the reliability and stability of asynchronous motor drives under degraded operating conditions. To achieve this, a fuzzy logic controller is designed. The controller adaptively adjusts control actions by integrating heuristic rules and membership functions capable of representing system uncertainties, thereby ensuring dynamic adaptability to unexpected disturbances. The methodology consists of designing the fuzzy controller and evaluating its performance through MATLAB/Simulink simulations. Comparative analyses are conducted against classical vector control and PID schemes. The evaluation criteria include voltage stability, dynamic response, and harmonic distortion under scenarios such as sensor faults and load disturbances. Simulation results demonstrate that the proposed fuzzy?based approach maintains voltage stability and ensures satisfactory dynamic performance even in the presence of sensor faults and load variations. Furthermore, comparative analysis highlights superior tolerance to defects and a reduction of harmonic distortion compared with conventional control strategies. In conclusion, fuzzy logic control emerges as a practical and effective solution for industrial applications requiring high reliability. The proposed method improves fault tolerance and system robustness. Future work will focus on experimental validation and integration with hybrid artificial intelligence techniques, paving the way for deployment in critical industrial environments.
Abstract: Asynchronous motor drives are extensively used in industrial applications due to their robustness, efficiency, and relatively low cost. However, their performance can be significantly degraded by sensor failures, parameter variations, or power supply disturbances, which compromise reliability and voltage stability. The objective of this research is...
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Research Article
Experimental Analysis and Adaptive Intelligent Load Frequency Control (Case Study of Shiroro Hydroelectric Power Generation Station, Nigeria)
Issue:
Volume 15, Issue 4, August 2026
Pages:
79-102
Received:
13 June 2026
Accepted:
6 August 2026
Published:
20 September 2026
Abstract: The stabilization of electricity is depended on absolute load frequency control. Rather than complicated advanced control techniques, this paper presents an adaptive intelligent load frequency control (AI-LFC) scheme that can be applied to improve the control performance of hydroelectric power generation plant in the presence of environmental disturbances. The AI-LFC scheme has been proposed for the adaptive control of a well-established Shiroro hydroelectric power generation Station located in Niger State, Nigeria as the case study. This study begins with 3-year data acquisition and experimental data analysis of the key sixteen parameters from the Shiroro Hydroelectric Power Plc in Niger State, Nigeria. Five important output control parameters have been identified to ensure the smooth operation and efficient control of the HPGP to maintain stable grid frequency. Prescribed reference trajectory tracking of the output predictions of the five control parameters as well as the output prediction errors have been used to evaluate the performance of the proposed AI-LFC scheme against that of a well-tuned artificial neural network-based proportional-integral-derivative (NN-based PID) controller for performance comparison purposes. The simulation results show that the proposed AI-LFC scheme outperforms the NN-based PID controller in terms of absolute tracking of the prescribed reference trajectory with absolute zero output predictions errors. The NN-based PID controller exhibits larger output prediction errors, overshoots, non-minimum and oscillatory behaviours, and require significant amount of control efforts to track the desired reference trajectory with occasional inability to reach the desired prescribed reference trajectory. The proposed AI-LFC scheme has been successfully formulated, implemented and validated for the modeling and control of the Shiroro hydroelectric power generation plant as a case study. The proposed AI-LFC outperforms a well-tuned NN-based PID controller and offers promising optimal adaptive control potentials that could be adapted for direct modeling and adaptive control of renewable systems.
Abstract: The stabilization of electricity is depended on absolute load frequency control. Rather than complicated advanced control techniques, this paper presents an adaptive intelligent load frequency control (AI-LFC) scheme that can be applied to improve the control performance of hydroelectric power generation plant in the presence of environmental distu...
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