Browsing by Author "Yu, XH"
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Article Citation Count: 1A robust on-line learning algorithm for intelligent control systems(John Wiley & Sons Ltd, 2003) Özbek, Mehmet Efe; Kaynak, O; Wilamowski, BM; Yu, XH; Department of Electrical & Electronics EngineeringThis paper describes a novel error extraction approach for exploiting the strength of Levenberg-Marquardt (LM) optimization technique in intelligent control systems: Since the target value of the control signal is unknown, tuning of the controller parameters becomes a tedious task if the knowledge about the system and the environment is limited. The suggested methodology utilizes the sliding model control (SMC) technique. The error extraction scheme postulates the form of error on the applied control signal using the discrepancy from the prescribed reaching dynamics. The devised approach has been tested on the non-linear Duffing oscillator, which has been forced to follow a periodic orbit radically different from the natural one. The results obtained through a series of simulations have confirmed the high precision and robustness advantages without knowing the analytical details of the system under investigation. The issues of observation noise and the stability in the parametric space have approximately been addressed from the point of SMC perspective. Copyright (C) 2003 John Wiley Sons, Ltd.Article Citation Count: 45Variable structure control of a class of uncertain systems(Pergamon-elsevier Science Ltd, 2004) Özbek, Mehmet Efe; Ünsal, C; Kaynak, O; Yu, XH; Department of Electrical & Electronics EngineeringThis brief paper proposes a method for tuning the parameters of a variable structure controller. The approach presented extracts the error at the output of the controller and applies a nonlinear tuning law using this error measure. The adaptation mechanism drives the state tracking error vector to the sliding hypersurface and maintains the sliding mode. In the simulations, the approach presented has been tested on the control of Dulling oscillator and the analytical claims have been justified under the existence of measurement noise, uncertainty and large nonzero initial errors. (C) 2003 Elsevier Ltd. All rights reserved.