نشریه مهندسی برق
سال پنجاه و سوم شماره 2 (پیاپی 104، تابستان 1402)

In this paper, a novel output redefinition is presented for attitude control of a flexible spacecraft. Attitude maneuvers induce undesirable vibration in the flexible appendages which degrades attitude control system performance. The attitude pointing and stabilization requirements of today advance space missions show the undesirable vibration suppression importance. If the flexible appendage tip-point is selected as the system output, the system model is nonminimum phase. The output redefinition method is an effective method to make the model minimum phase. By a deeper insight about the output redefinition method, a novel systematic method is presented such that the attitude control system performance is modified. Then, a nonlinear H∞ attitude control method is desined based on the redefined model system. For the situation that modal variable are not measurable, a modal observer is presented. The simulation results verify the presented method effectiveness in a flexible spacecraft attitude control problem in presence of mode uncertainties and unknown disturbance.

In this paper the analysis and design of an operational amplifier is presented. This amplifier can follow the input signal from ground to supply voltage at its input and output. Based on the level of the input common mode voltage, one of two pMOS and nMOS differential pairs are selected to amplify the input signal. This causes the amplifier performance is independent of the drain current relationship of the input differential pairs transistors. Both static and dynamic currents of the class AB output stage are provided by two translinear loops with minimum sensitivity to process, temperature and supply voltage variations. The simulation results, in a 0.18  CMOS technology, show that the amplifier in nominal condition, has about 80 dB dc voltage gain, 51 MHz unity gain bandwidth and 63  phase margin while its static power consumption is almost 1 mW. For input common mode voltage changes between ground to the supply voltage, the maximum relative variations of the input differential pairs transconductances, amplifier’s dc voltage gain, phase margin and unity gain bandwidth are , ,  and , respectively. This amplifier as a buffer can drive a load consisting of parallel connection of a resistor  and a capacitor 20 pF.

In this paper, the magnetic field outside human head was obtained by method of forward problem solution and using new four layer eccentric spherical conductor model for human head. The use of numerical methods for human head has little accuracy due to dimensions of human head and low frequency of brain waves. Using cocentric models due to the difference in the actual geometric shape of different layers of human head and cocentric spheres causes errors in estimating source location. The three layer model can cause errors due to ignoring difference in conductivity between the brain and the cerebrospinal fluid around the brain. Therefore, the results related to eccentric four layer spherical model in this paper are more accurate than the results related to three layer spherical model. The spherical model has isotropic layers, that is, the electrical conductivity coefficients of each layer are the same at all points of that layer. The equations were obtained analytically using a dipole as a source. According to our results, it was observed that the magnetic field graph of the new model has little changes compared with to other models.

This paper proposes a controller capable of satisfying the output constraints while providing a near-optimal performance. The method is designed for square nonlinear systems with stable internal dynamics. To this end, using a primary performance index and Taylor approximation, the problem is approximated by a constrained programming, whose constraints are written with the aid of Control Barrier Function (CBF) to ensure the output restrictions. As a result, a constrained near-optimal performance without encountering difficulties in Hamilton-Jacobi-Bellman (HJB) equations is obtained. In order to overcome the model uncertainties, which appear in the optimization problem, an adaptive structure is formulated. The online solution of the constrained optimization is obtained using a Projection Recurrent Neural Network (PRNN). As a result, a closed-form solution is provided that can be simply implemented without requiring additional solvers or toolboxes. Stability of the proposed method and constraints satisfaction are addressed thoroughly. Finally, effectiveness of the proposed method in realizing the aforementioned aims are illustrated through simulations on trajectory control of a surface vessel system and a comparative study on the constrained stabilization of a pendulum. The first simulation example shows the effectiveness of the method in constrained tracking, while the second example confirms the proper performance of the controller in stabilization and regulation applications.

The Internet of Things is a new technology that communicates with the surrounding objects through the Internet and is used for the purpose of remote measurement and control. In the field of Internet of Things (IoT) network security, it is very important to accurately identify the types of attacks on these networks that are launched by zombie hosts under the control of the attacker. In this article, a new neural network is proposed to improve the detection of intrusion into the Internet of Things network based on the ALEXNET convolutional neural network and chaotic krill optimization algorithm (MONANET). In the MONANET network, in order to improve the accuracy in detecting intrusion into the IoT network and not need to manually adjust the parameters, the hyperparameters of the neural network are dynamically selected using the chaotic krill algorithm. The value of the loss function of the validation set obtained from the first training of the neural network model using the Danmini doorbell dataset is considered as the CKH fitness value. The comprehensive performance of the proposed network and GRU, ANN, SVM, LSTM, R-CNN, and APSO-CNN algorithms have been compared in five evaluation indices and 12 times independent experiments. The obtained results show the improvement of intrusion detection to the Internet of Things network. The proposed algorithm has been able to accurately detect %99.89 attacks on the Internet of Things network. The experimental results show the superiority of the proposed method over other knowledge boundary methods in terms of improving classification accuracy.

Smart grids are complex, nonlinear and uncertain systems. Smart grids may consist of one or more cost functions, in which their optimal values have to be obtained. Moreover. In these systems the optimal condition has to be find with small computation burden, high accuracy, and relatively fast in real time. In this paper, a novel approach is proposed to find the minimum value of the total cost for the energy consumption in smart grids, in which the total cost is considered as uncertain objective function. The advantages of this technique are its stability, fast convergence, insensitivity with respect to initial condition and real time implementation. Some simulation results are provided to show the effectiveness of the proposed approach.

Easy access to social media enables users to express their opinions and ideology about various topics like news, videos, and personalities freely, without any fear, and often in an offensive manner. It is a vital task to detect comments with offensive language on social media platforms and relies on a complete and comprehensive tagged dataset. Therefore, in this paper, we introduce and make publicly available PerBOLD, a new Persian comment dataset collected from Instagram as a popular platform among Iranian. We follow a two-level manual annotation process in order to determine whether a comment has offensive language or not and fine-grained tags of different types of offensive language. Furthermore, we present some interesting aspects of data and analysis them.