Transactions on Machine Intelligence

Transactions on Machine Intelligence

Intelligent Speed Control of an Induction Motor Drive Using an Online TLBO-Based Fuzzy PI Tuning Strategy

Document Type : Original Article

Authors
1 Department of Computer Engineering, Middle East Technical University (METU), Ankara, Türkiye
2 Department of Electrical and Electronics Engineering, Boğaziçi University, Istanbul, Türkiye
Abstract
This paper proposes an intelligent speed control strategy for a three-phase induction motor drive based on indirect field-oriented control (IFOC), fuzzy logic, and the Teaching–Learning-Based Optimization (TLBO) algorithm. The proposed approach is developed to improve the dynamic performance of the motor drive by optimally tuning the proportional–integral (PI) controller parameters in response to variations in the speed error. In the proposed strategy, fuzzy logic is employed to adapt the controller parameters according to the instantaneous control error and its variation, while the TLBO algorithm is utilized to determine suitable controller parameters and enhance the overall tuning process. The complete control scheme is implemented and evaluated in the MATLAB/Simulink environment under different operating conditions. The performance of the proposed controller is assessed in terms of speed tracking, transient response, overshoot, settling time, and disturbance rejection. The obtained results are compared with those of a conventional PI controller tuned using the Ziegler–Nichols method. The simulation results demonstrate that the proposed online TLBO-based fuzzy tuning strategy provides improved speed-tracking performance and enhanced dynamic behavior compared with the conventional tuning approach. These findings indicate that the integration of fuzzy adaptation with TLBO optimization can provide an effective and flexible control solution for induction motor drive applications.
Keywords

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Volume 8, Issue 4
Autumn 2025
Pages 223-232

  • Receive Date 08 August 2025
  • Revise Date 18 October 2025
  • Accept Date 23 November 2025