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In this paper, a disturbance observer based (DOB) anti-disturbance dynamical tracking control is discussed for un-manned aerial vehicles (UAVs) with unknown disturbances. The designed DOBC algorithm is combined with the T-S fuzzy disturbance models so as to achieve the track of the velocity and the pitching angle of UAVs. On one hand, the T-S fuzzy models are employed to describe the unmodeled nonlinear disturbances, in which a DOB on T-S disturbance model is designed to estimate unknown disturbances. On the other hand, a DOB PI-type feedback controller is proposed to ensure the UAV models stability and convergence of the tracking error to zero. Finally, the simulation results further verify the designed DOB algorithm can obtain the satisfactory anti-disturbance and dynamical tracking performance.

In this paper, a novel DOB identification and control algorithm is developed for hypersonic flight vehicle. The DOBC technique is combined with the DNNs Models so as to identify attitude and velocity in the presence of disturbance. By using an adaptive method to adjust the weight matrices and compensate the unknown parameters, the control input is built with the Nussbaum gain matrix and feedback control gain. The stability proof is provided by using Lyapunov method. Finally, the simulation results shows DOBC technique is combined with DNNs models can obtained satisfactory dynamical identification and anti-disturbance performance.

This paper discusses the novel anti-disturbance control algorithm for hypersonic flight vehicle (HFV) models by using neural network (NN) identifier. Different from those existed anti-disturbance results, the unknown exogenous disturbances in HFV models are assumed to be described by the designed NNs with adjustable parameters. Furthermore, the disturbance-observer-based-control (DOBC) algorithm with adaptive regulation laws is thus presented to estimate the nonlinear disturbances. By integrating the estimated value of disturbances with the PI feedback control input, a composite controller based on convex optimization theory is generated to ensure the satisfactory stability and dynamical tacking convergence of HFV models. Finally, a numerical example for HFV models is included to illustrate the effectiveness of the theoretical results.