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A Self-Tuning Minimal-Rule Fuzzy Logic Controller for High-Performance Induction Motor Drives
Fuad Alhaj Omar1, Nihat Pamuk2, Talha Enes Gümüş3
1Department of Electric and Energy, Zonguldak Bülent Ecevit University, Zonguldak 67100, Türkiye.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
A novel self-tuning fuzzy logic controller for induction motors offers high performance with minimal rules. This approach significantly reduces complexity while maintaining excellent dynamic response and disturbance rejection.
Area of Science:
- Electrical Engineering
- Control Systems
- Artificial Intelligence
Background:
- High-performance induction motor drives require sophisticated control strategies.
- Conventional fuzzy logic controllers (FLCs) can be computationally intensive due to numerous rules.
- Existing reduced-rule FLCs often lack adaptability or require complex online adjustments.
Purpose of the Study:
- To present a self-tuning, minimal-rule fuzzy logic controller for induction motor drives.
- To achieve high dynamic performance and effective disturbance rejection with simplified control structure.
- To reduce computational complexity compared to conventional and full-rule FLCs.
Main Methods:
- A nine-rule Mamdani fuzzy inference structure combined with a bounded online output gain adaptation.
- Offline determination of nominal gain and adaptation sensitivity using Particle Swarm Optimization (PSO).
- Closed-loop behavior analysis using a discrete-time Lyapunov framework for induction motor dynamics.
Main Results:
- Achieved a 0.15 s rise time, 0.26 s settling time, and 4 RPM tracking error.
- Demonstrated negligible overshoot and approximately 0.44 Nm torque ripple.
- Reduced fuzzy-rule evaluations by 81.6% (from 49 to 9 rules) compared to a classical FLC.
Conclusions:
- The proposed self-tuning minimal-rule FLC offers a favorable trade-off between performance and simplicity.
- The controller exhibits robust dynamic performance and disturbance rejection in simulations.
- Further validation through generated-code SIL, HIL testing, and experimental implementation is recommended.
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