Optimized control for attitude variable structure of UAV based on recurrent wavelet neural networks
CHEN Gui-ping
Abstract：The attitude control of unmanned aerial vehicle ( UAV ) is susceptible to the external air flow disturbance and model parameter perturbation. In order to improve the accuracy and stability of attitude control, an optimized robust control law was proposed based on variable structure control and recurrent wavelet neural networks. The attitude motion model for UAV was constructed and analyzed. A stabilized control law for the attitude motion of UAV was designed with the variable structure control. The recurrent wavelet neural networks were added into the control closed loop. Therefore, the variable structure control law could be optimized and the dependence of control law on the model accuracy could be weakened. In addition, the comparison with the traditional methods was performed in the simulation validation. The results show that the proposed control law can improve the stability of attitude control of UAV, and has strong robustness, shorter convergence time and less energy consumption, which proves the effectiveness and feasibility of the proposed method.
Keywords：unmanned aerial vehicleattitude controlvariable structure controlrecurrent wavelet neural networkoptimized controlstabilityrobustnessenergy consumption
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 94-98 )