The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper applies a recurrent higher-order neural network for sliding-mode pinning control of complex networks for achieving trajectory tracking. This control strategy does not require having the same coupling strength for all node connections on the network. The tracking effectiveness and dynamical behavior of the controlled network is illustrated via simulations.
Due to energy demand increasing and environmental issues, which are becoming more critical with time, integration of clean energy resources has become an important subject of research. Microgrids can be defined as an interconnection of energy sources at distribution level, which preferably includes renewable ones, and which are key in the evolution of distributed generation systems. This work deals...
This paper presents a discrete-time sliding mode control design based on a neural model for induction motors. A Recurrent High Order Neural Network (RHONN) trained with an Extended Kalman Filter (EKF) is used to identify the model. The sliding mode controller is designed to force the system to track a torque reference and a flux magnitude. Then, a reduced order observer is designed for rotor fluxes...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.