Enhancing power efficiency in BLDC motor drives for drones using multiview learning with hybrid optimization algorithms.

This study explores power efficiency enhancements in BLDC motor drives for drone applications, taking cues from the foraging behavior of the electric eel and gooseneck barnacle optimization. Employing multi-view learning techniques, it evaluates various performance metrics such as torque speed and power, revealing substantial improvements over the existing methods. Fitness improvement rates per iteration for Eel Foraging (0.05) and Gooseneck Barnacle Optimization (0.04) demonstrate their superior efficiency over standard evolutionary algorithms (0.02-0.10). Analyzing torque speed with control further highlights the effectiveness of Eel Foraging and Gooseneck Barnacle Optimization through Multi-View Learning. At a speed of 2000 rpm, the torque generated by Eel Foraging was 15 Nm, whereas the Gooseneck Barnacle Optimization yielded 14 Nm. This difference underscores the nuanced optimization strategies employed by each method. Additionally, the power rating selection process, based on ideal torque-speed characteristics, provides practical insights. For instance, at a torque of 3.5 Nm, the intermittent power rating for both Eel Foraging and Gooseneck Barnacle Optimization is 265 W, aligned with their efficiency-driven design principles. Utilizing these characteristics as a reference, this study defines optimal performance benchmarks for future BLDC motor-drive designs. Through mathematical optimization techniques, the positions were adjusted based on the fitness values and previous states. This process ensures that Eel Foraging and Gooseneck Barnacle Optimization remain adaptable to changing conditions, with solutions consistently improving over iterations. Moreover, the evaluation of fitness impacts provides critical feedback for further refinement, driving the optimization process forward.