Attitude Robust Sliding Mode Control of Unmanned Helicopter Based on R-CFO

Aiming at the attitude control problem of unmanned helicopter (UH), a robust sliding mode controller based on compensation function observer (CFO) with radial basis function (RBF) neural network (R-CFO) was proposed. Considering that the UH system was affected by uncertainties such as internal and external disturbances and modeling unknowns, a CFO based on RBF neural network was introduced to estimate and compensate the uncertainty factors of the system in real time, thus realizing the robustness of the system to disturbances and high adaptability to the environment. At the same time, disturbance and uncertainty by introducing switching function were eliminated by sliding mode control. And the stability of the control system was proved by Lyapunov theory. Numerical simulation and digital signal processing an control engineering (dSPACE) hardware-in-the-loop simulation results showed that the robustness and stability of attitude control for unmanned helicopters could be guaranteed by the proposed control method.