This paper studies the problem of prescribed performance adaptive neural control for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems in the presence of external disturbances and input saturation based on a disturbance observer. The system uncertainties are tackled by neural network (NN) approximation. To handle unknown disturbances, a Nussbaum disturbance observer is presented. By incorporating the disturbance observer and NNs, an adaptive prescribed performance neural control scheme is further developed. Then, the expected asymptotically convergent tracking errors between system output signals and desired signals are achieved. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.

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In this paper, the flight control of an unmanned aerial vehicle (UAV), quadrotor, in the presence of high-order disturbances is presented. Since during flight, UAV faces enormous, and various kinds of disturbance, the effect of such disturbances becomes vital for consideration during the development of disturbance observer (DO) based tracking control scheme. To obtain the desired tracking performance, standard sliding mode control (SMC) method is utilized while for disturbance estimation, DO based on Simpson's approximation is developed and incorporated with SMC. Furthermore, in this paper, both matched and mismatched disturbances are considered. Hence, a matrix associated with disturbances is invoked in the system model. To show the effective and desired control performance of developed disturbance observer-based control (DOBC) scheme, extensive simulations are conducted, followed by presenting the results in the paper.

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