Adaptive sliding mode robust control based on multi-dimensional Taylor network for trajectory tracking of quadrotor UAV

In this study, multi-dimensional Taylor network (MTN) inspired sliding mode robust control theory based adaptation laws are proposed to realise trajectory tracking for a quadrotor unmanned aerial vehicle. Compared with existing methods, the composite disturbance considered in this study includes external multiple interference and fuselage parameter perturbation, which is more in accord with reality. Through a linear state transformation, the uncertain coefficients and unknown external disturbances are grouped together and the original kinetic model is decoupled into two subsystems of position and attitude that make the control design become feasible. MTNs are used to compensate the lumped non-linearities, and the adaptive sliding mode technique is employed to construct the controller. Different from the controllers based on neural networks, MTN contains only addition and multiplication, which greatly lessens the computational complexity and improves the real-time performance. By the Lyapunov theory, the position tracking error, attitude tracking error and fuselage load identification error are bounded in probability have been proved and the stability of the system is guaranteed. Simulation studies demonstrate the effectiveness and superiority of the proposed trajectory tracking control scheme.