A Self-Tuning Minimal-Rule Fuzzy Logic Controller for High-Performance Induction Motor Drives

This paper presents a self-tuning minimal-rule fuzzy logic controller for high-performance induction motor drives operating under field-oriented control. Unlike conventional full-rule fuzzy controllers and reduced-rule designs with fixed post-design scaling, the proposed method combines a fixed nine-rule Mamdani inference structure with a bounded online output gain adaptation mechanism. The nominal gain and adaptation sensitivity are determined offline using Particle Swarm Optimization, thereby retaining operating-condition responsiveness without requiring online optimization, rule reconstruction, or membership-function retuning. The closed-loop behavior is analyzed using a discrete-time Lyapunov framework derived from the induction motor mechanical dynamics under bounded disturbances. The controller is evaluated through fixed-step simulations incorporating measurement noise, 12-bit signal quantization, and a one-sample computational delay. Comparative results against a conventional PI controller and a classical 49-rule fuzzy controller show that the proposed scheme achieves a rise time of 0.15 s, a settling time of 0.26 s, a post-transient mean absolute tracking error of 4 RPM, negligible overshoot, and a torque ripple of approximately 0.44 Nm. Relative to the classical 49-rule FLC, the proposed design reduces the maximum number of fuzzy-rule evaluations per control update from 49 to 9, corresponding to an 81.6% reduction in structural fuzzy-inference complexity. The results indicate a favorable simulation-level trade-off between dynamic performance, disturbance rejection, and structural algorithmic simplicity. Generated-code SIL, Hardware-in-the-Loop testing, target-processor timing measurements, and experimental implementation remain necessary to establish practical embedded feasibility.