Adaptive Integral Sliding Mode Control for Symmetric UAV with Mismatched Disturbances Based on an Improved Recurrent Neural Network
Year: 2025
Type: article
Abstract: This study proposes a sliding-mode-based adaptive control framework for symmetric quad-rotor altitude and attitude tracking under parametric uncertainties and mismatched disturbances. To address mismatched disturbances, a finite-time disturbance observer (DO) is integrated into a high-order terminal sliding mode manifold design. While conventional sliding mode control suffers from dependence on precise dynamic models that are unavailable in quad-rotor applications, we devise a fully connected double hidden layer recurrent neural network (FCDHRNN) with full interlayer feedback to approximate unmodeled dynamics. The structure uses double hidden layer connections to strengthen the approximation ability, and its double-layer structure achieves higher accuracy and generalization ability and uses fewer neurons than the single-hidden-layer network. Through Lyapunov stability analysis, weight adaptation laws are rigorously derived to guarantee finite-time convergence of both tracking errors and estimation residuals. Simulation results show that the proposed scheme has superior performance compared with the existing quad-rotor control scheme.
Source: Symmetry
DOI: 10.3390/sym17101720
Authors: Shanping Wang, Haicheng Wan, Ping Wang, Wendong Li
Institution: Shandong Huayu University of Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4415138216