Quadrotor attitude control under perturbations with neural network and state observer
Year: 2025
Type: article
Abstract: This paper proposes an integrated control framework for quadrotor attitude regulation that addresses the challenges of nonlinear dynamics, external disturbances, internal uncertainties, and real-time adaptability. The framework incorporates an improved sparrow search algorithm to optimize the parameters of a neural network, enabling accurate approximation of complex system behaviors. A linear extended state observer is employed to estimate and compensate for time-varying external disturbances in real time, while a dynamic regulation module ensures rapid and stable system response. Simulation results demonstrate that the proposed method significantly enhances control accuracy and robustness under complex and uncertain conditions, outperforming other state-of-the-art methods.
Source: Results in Engineering
Authors: Ende Wang, Ziyao Chen, Jiageng Liu, Boxuan Sun
Institution: Shenyang Ligong University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,940
OpenAlex ID: W4416451372