Sharif Digital Repository

Ducted fan aerial vehicles have drawn many attentions in the world because of their successful involvement in non-traditional reconnaissance and surveillance missions. However, due to inherent dynamic uncertainties as well as inconsistent responses, significant control challenges are still to be addressed. In this study, a non-linear dynamic model for ducted fan is firstly proposed to be employed for control design. This model is then validated by performing a series of standard simulation scenarios. Afterwards, an adaptive control method, named as model reference is utilized to design perfect controllers in hover as well as vertical flight. The capability of the adaptive laws to update the... 

In this study, an impulsive state feedback controller has been proposed for the treatment of hepatitis-C-infected patients under Pegylated-Interferon (PEG-IFN-α2b) therapy. The Neumann model has been utilized as the representative of the hepatitis C virus (HCV) dynamics. In order to consider the drug efficacy variation between injections, the pharmacokinetics/pharmacodynamics (PK/PD) equations have been included in the model. The impulsive nature of the drug injection also has been considered in the disease dynamics. In the proposed treatment method, the drug dose limitation has been addressed as an input nonlinearity. The asymptotical stability of the control method under the impulsive... 

System Identification is a key technology for the development and integration of modern engineering systems including unconventional flying vehicles. These systems are highly parametric with complex dynamics and nonlinearities. Ducted fans are special class of these vehicles that can take off vertically, hover and cruise at very low speed. In this paper, an exact equivalent linear system is found from the non-linear dynamic model of a ducted fan by use of frequency response identification. Here, power spectral density analysis is performed, using CIFER software, to evaluate the input-output responses in hover and to derive the transfer functions based on the coherence criterion. Then,... 

Tight unmanned aerial vehicle (UAV) autonomous missions such as formation flight and aerial refueling (AR) requires an active controller that works in conjunction with a precise vision-based sensor that is able to extract In-front aircraft relative position and orientation from captured images. A key point in implementing such a sensor is its robustness in the presence of noises and other uncertainties. In this paper, a new vision-based algorithm that uses neural networks to estimate the In-front aircraft relative orientation and position is developed. The accuracy and robustness of the proposed algorithm has been validated via a detailed modeling and a complete virtual environment based on...