Adaptive Hybrid Fuzzy–Neural PID Control of Speed Regulation and Torque Ripple Reduction of BLDC Motors
DOI:
https://doi.org/10.12928/biste.v8i3.16182Keywords:
BLDC Motor, PID Control, NN-PID Controller, Fuzzy Control, Intelligent Algorithms, Optimization, Speed ControlAbstract
This paper focuses on the control of Brushless Direct Current (BLDC) motors utilizing an enhanced fuzzy control and Neural Network (NN) located Proportional–Integral–Derivative (PID) control arrangement that acts as real-time mistake adaptation and adjustment to regulate engine speed. The intelligent optimization algorithm is also used to embellish the action of the fuzzy PID controller. BLDC motors are settled in production, conveyance, and meet extreme-precision requests next to their plain creation, reliable movement, and superior speed control competence. Improving the control veracity of BLDC motors is a main research issue, and some improvements have been made in the current age. Conventional (PID) control algorithms have a simple form and expansive relevance and are usually used for BLDC engine speed control. However, these algorithms do not efficiently detect differences in load conditions. To address this restraint, an FLC and interconnected system-based PID control means is executed to regulate and correct control errors in real time. In order to improve BLDC motor dynamic performance across a range of load circumstances, the suggested hybrid NN-PID controller will integrate FLC adaptation with NN learning. Simulation results show that the projected means correct the speed error at 0.2 s from 22.3 to −0.102, from 22.5 to −0.305, and from 38.5 to −13.474, distinguished by accompanying (NN), FLC rationale, and conventional PID controllers, individually. In addition, the projected approach reduces torque ripple by 64.13%, 68.3%, and 74.56% distinguished with NN, fuzzy sense, and PID controllers. The substitution results represent the usefulness of the pertinent whole form in threatening speed, error, and torque ripple.
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