Resilient Flight Control for a 32g Nano Helicopter

Nano helicopter unmanned air vehicles (NHUAVs) offer significant advantages due to their compact size, maneuverability, and cost-effectiveness, making them versatile and flexible platforms for various applications. Despite these merits, achieving full autonomy in NHUAV control presents challenges including multivariable, strongly coupled nonlinear systems, susceptibility to external disturbances, uncertainty in system dynamics, and altitude loss during sharp turns. This article introduces a novel robust adaptive bidirectional fuzzy brain emotional learning (BFBEL) control strategy to address these challenges and achieve stable pose control and highly accurate trajectory tracking for uncertain NHUAVs. The BFBEL controller integrates fuzzy inference, a neural network, and a bidirectional brain emotional learning (BBEL) mechanism, enabling rapid adaptation and precise flight control. Implemented on a customized 32g NHUAV platform, the proposed control algorithm demonstrates remarkable performance in the presence of wind disturbance and system uncertainty. The obtained results underscore the superiority of BFBEL, showcasing significant advancements over self-adaptive sliding surface-based Takagi–Sugeno fuzzy control and conventional PID control approaches in terms of robustness to disturbances and trajectory tracking precision.