基于多层感知器的四旋翼无人机扰动下自适应滑模控制

This paper introduces a novel adaptive sliding mode control (SMC) framework for quadrotor unmanned aerial vehicles (UAVs) operating in the presence of external disturbances and parametric uncertainties. The proposed approach leverages a multi-layer perceptron (MLP) neural network to dynamically tune the SMC parameters in real-time. This synergistic integration of MLP with SMC enables adaptive, efficient, robust, and energy-efficient control, significantly enhancing system performance. By continuously adjusting the SMC controller parameters based on real-time system feedback, the MLP effectively mitigates the impact of external disturbances and parameter uncertainties, achieving optimal hyperparameter values for enhanced trajectory tracking accuracy. The neural network empowers the controller to adapt seamlessly to dynamic changes in system behavior and environmental conditions. Another key contribution of this research lies in the substantial reduction of the chattering phenomenon, a common limitation of traditional SMC systems. Through rigorous simulations, the proposed controller demonstrates superior stability and robustness under diverse external disturbances and dynamic operating conditions. The rapid adaptability of the controller to changing environments surpasses the performance of conventional methods. This work underscores the significant potential of integrating artificial intelligence techniques with classical control methodologies to enhance the resilience and adaptability of UAV control systems in complex and unpredictable scenarios.