Efficient BLDC motor control using hybrid crayfish optimization and sine cosine algorithm in renewable energy systems

This research focuses on optimization of Brushless DC motors for applications in renewable energy systems by using the
proposed new hybrid innovative technique “CraySine Control” in addressing the challenge of real-time adaptation and
dynamic conditions. In the existing interface between the power management system and the BLDC motor controller,
there is an instability occurs due to renewable energy fluctuations, causing delays or overshooting, reducing efficiency and
performance. To solve this issue, a novel Adaptive Crayfish-MPC Neuro-Fuzzy Control (ACM-NFC) is introduced, which
improves BLDC motor stability and efficiency by dynamically adapting to renewable energy fluctuations, avoiding delays
or overshooting by using Model Predictive Control (MPC) for forecasting purpose, Adaptive Neuro-Fuzzy Inference
System (ANFIS) for adaptation purpose and Hybrid Crayfish Optimization Algorithm (CFO) for optimization purpose.
Moreover, inefficient BLDC motor control leads to energy overconsumption, as standard algorithms lack adaptability,
multi-objective optimization, and performance balance. To address this issue, a novel Hybrid Fuzzy Sliding Mode Sine
Cosine Algorithm (HF-SCA) is introduced, which improves BLDC motor efficiency, stability, and responsiveness using
Fuzzy Logic Control (FLC) for adjustment purpose, Sliding Mode Control (SMC) for stability purpose, and Sine Cosine
Algorithm (SCA) for optimization purpose under fluctuating loads. The proposed model achieves high energy efficiency
(92.5%), minimal response time (30 ms), enhanced stability (0.92), and adaptability (0.925), optimizing BLDC motor
performance for reliable and sustainable renewable energy applications.