Sistem ve Kontrol Mühendisliği

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Browsing Sistem ve Kontrol Mühendisliği by Subject "Control theory."

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    Application of variable structure systems theory for training of intelligent systems
    (Thesis (M.S.) - Bogazici University. Institue for Graduate Studies in Science and Engineering, 2001., 2001.) Yıldıran, Uğur.; Kaynak, Okyay,
    Soft computing architectures with their extensive flexibility and strong mapping capabilities have been widely used for control of nonlinear systems. In this regard, error backpropogation and its derivatives have been the most popular and frequently employed schemes for parameter adjustment of these architectures. However, these schemes bring some serious problems together, like instability of closed loop system and sensitivity to uncertainties, which must be carefully addressed by a system designer. In order to alleviate these problems, recently, Efe has proposed a control strategy in which parameters of intelligent controllers are updated by a continuous-time robust parameters adjustment mechanism in order to robustify and stabilize the closed loop system dynamics. The results obtained for a two link SCARA robot in this study show that the proposed method is successful in achieving the control objectives. In this thesis, the methodology proposed by Efe is investigated for first order nonlinear systems. Based on the results, it has been observed that the time evolution of input-output curves of different structures show similar characteristics. Moreover, a modification is proposed for update mechanism of all architectures in order to prevent unbounded parameter evolution problem which occurs in the original algorithm. Lastly, based on the results for different systems, it has been concluded that the Adaptive Linear Element is the most suitable architecture for the control systems investigated because of its simplicity.
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    LPV controller synthesis for the rotary inverted pendulum
    (Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2011., 2011.) Kırcalar, Mustafa Emre.; Eşkinat, Eşref.
    Linear parameter varying (LPV) controller design for nonlinear systems is mostly considered as challenging. Moreover, the available methods and tools suggested in the literature are scarce and not well tested on physical systems. In this thesis, the design of the LPV controller for the Rotary Inverted Pendulum (RIP) is presented. Starting from a linear fractional transformation structure (LFT) of the LPV plant, which is obtained by nonlinear dynamical equations of the RIP, controller synthesis Linear Matrix Inequality (LMI) conditions are developed. By involving LMIs, based on LPV synthesis theory, the controller design procedure is performed. Additionally, the designed LPV controller performance is evaluated by the provided simulation and experimental results for di erent conditions.|Keywords : Control systems, Non-linear equations, Nonlinear systems, Robust control, Control engineering, Control theory, Pendulum system
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    Targeting in chaos using analytically described clusters
    (Thesis (M.S.) - Bogazici University. Institue for Graduate Studies in Science and Engineering, 2002., 2002.) Sütçü, Yağız.; Denizhan, Yağmur.
    The OGY method provides a simple but powerfbl approach of controlling chaotic dynamics. This method can stabilise inherently unstable equilibrium modes of dissipative chaotic systems under the lack of knowledge about the system equations. However, it has the typical drawback of a long waiting time until the system starting from random initial conditions enters the close neighbourhood of the equilibrium mode to be stabilised, where the controller can be activated. The reduction of this drawback is known under the name of targeting. The Extended Control Regions method is a targeting approach, which can operate under the Iakof knowledge about the system equations by employing local models of the system dynamics extracted from empirical data. The method is based on the idea of identifiing and modelling those regions of the phase space, starting fi-om which the system can be steered to a close neighbourhood of the target within a few steps applying sinall perturbatiotns in the control parameters. So far, the modelling of the system dynamics within these phase space regions have been realised using artificial neural networks. In this study, two different strategies are developed in order to realise the clustered version of the Extended Control Regions method on basis of simple analytical models rather than neural networks. Each cluster obtained the gathered data is analytically described as a hyper-ellipsoid. Subsequently, the analytical models of the clusters are used for targeting purposes by applying small discrete variations in the control parameter. Simulation results on several chaotic systems with single control parameter show that the proposed method can achieve targeting using less memory and computation time than the Clustered Extended Control Regions method on cost of a slower targeting performance.