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Showing 2 results for Babaie

R. Babaie, A. F. Ehyaei,
Volume 15, Issue 2 (June 2019)

In this paper, using the State Dependent Riccati Equation (SDRE) method, we propose a Robust Optimal Integral Sliding Mode Controller (ROISMC) to guarantee an optimal control law for a quadrotor which has become increasingly important by virtue of its high degrees of manoeuvres ability in presence of unknown time-varying external disturbances and actuator fault. The robustness of the controller is ensured by an Integral Sliding Mode Controller (ISMC). Subsequently, based on Luenberger linear state estimator, the control algorithm is reformed and the actuator’s faults are detected. Moreover, design of the controller is based on Lyapunov method which can provide the stability of all system states during the tracking of the desired trajectory. The stability of suggested algorithm is verified via the execution of sudden maneuvers subjected to forcible wind disturbance and actuator faults while performing accurate attitude and position tracking by running an extensive numerical simulation. It is comprehended that the proposed optimal robust method can achieve much better tracking capability compared with conventional sliding mode controller.

P. Ramezanpour, M. Aghababaie, M. R. Mosavi, D. M. de Andrés,
Volume 18, Issue 2 (June 2022)

Through beamforming, the desired signal is estimated by calculating the weighted sum of the input signals of an array of antenna elements. In the classical beamforming methods, computing the optimal weight vector requires prior knowledge on the direction of arrival (DoA) of the desired signal sources. However, in practice, the DoA of the signal of interest is unknown. In this paper, we introduce two different deep-neural-network-based beamformers which can estimate the signal of interest while suppressing noise and interferences in two/three stages when the DoAs are unknown. Employing deep neural networks (DNNs) such as convolutional neural networks (CNNs) and bidirectional long short-term memory (bi-LSTM) networks enables the proposed method to have better performance than existing methods. In most cases, the output signal to interference and noise ratio (SINR) of the proposed beamformer is more than 10dB higher than the output SINR of the classical beamformers.

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