Browsing by Subject "MIMO systems"

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    Robust adaptive control of nonlinear systems with unknown state delay
    (2013) Bayrak A.; Tatlicioglu E.; Bidikli B.; Zergeroglu E.
    In this work, we propose a new robust adaptive controller for a class of multi-input multi-output nonlinear systems subject to uncertain state delay. The proposed method is proven to yield semi-global asymptotic tracking despite the presence of additive input and output disturbances and parametric uncertainty in the system dynamics. An adaptive desired system compensation in conjunction with a continuous nonlinear integral feedback component is utilized in the design of the controller and Lyapunov-based techniques, are used to prove that the tracking error is asymptotically driven to zero. Numerical simulation results are presented to demonstrate the effectiveness of the proposed method. © 2013 IEEE.
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    Experimental analysis and dynamic modeling of a photovoltaic module with porous fins
    (Elsevier Ltd, 2018) Selimefendigil F.; Bayrak F.; Oztop H.F.
    In this study, experimental analysis and performance predictions of solar photovoltaic (PV) module equipped with porous fins were performed. The experimental setup was tested in Technology Faculty of Firat University, Elazig of Turkey which is located at 36° and 42° North latitudes. The PV module was oriented facing south and tilted to an angle of 36° with respect to the horizontal in order to maximize the solar radiation incident on the glass cover. Experimental analysis was conducted for configurations where PV module is equipped with porous metal foams. A multi-input multi-output dynamic system based on artificial neural networks was obtained for the PV configuration with and without fin by using the measured data (ambient temperature, PV panels back surface temperatures, current, voltage, radiation and wind velocity) from the experimental test rig. It was observed that adding porous fins to the PV module results in performance enhancements. The developed mathematical model based on dynamic neural networks can be used for further development and performance predictions of these systems. © 2018 Elsevier Ltd