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SummaryGraphene, which as a new carbon material shows great potential for a range of applications because of its exceptional electronic and mechanical properties, becomes a matter of attention in these years. The use of graphene in nanoscale devices plays an important role in achieving more accurate and faster devices. Although there are lots of experimental studies in this area, there is a lack of analytical models. Quantum capacitance as one of the important properties of field effect transistors (FETs) is in our focus. The quantum capacitance of electrolyte-gated transistors (EGFETs) along with a relevant equivalent circuit is suggested in terms of Fermi velocity, carrier density, and fundamental physical quantities. The analytical model is compared with the experimental data and the mean absolute percentage error (MAPE) is calculated to be 11.82. In order to decrease the error, a new function of E composed of α and β parameters is suggested. In another attempt, the ant colony optimization (ACO) algorithm is implemented for optimization and development of an analytical model to obtain a more accurate capacitance model. To further confirm this viewpoint, based on the given results, the accuracy of the optimized model is more than 97% which is in an acceptable range of accuracy.

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In this study, output feedback sliding mode control (OFSMC) is proposed for a class of system with the uncertainties present in its output matrix. The uncertain output exist in practical system is mainly due to measurement error contributed by sensor noise or low measurement resolution. Sliding Mode Control (SMC) approach is able to provide a promisable solution due to its robustness toward system uncertainties and disturbances, However, OFSMC has imposed a greater challenge due to inaccessibility to all of the system states. In this work, an OFSMC is designed in which the actual system output will follow the desired trajectory in spite of the presence of uncertainties. The control law is designed based on Lyapunov function which the proposed controller guarantees the asymptotic convergence of the output. The sliding surface is formulated such that stability of the reduced-order system is maintained and the convex formulation of the problem is solved by using Linear Matrix Inequality (LMI). Simulation result based on a numerical example is obtained to demonstrate the efficacy of proposed method.

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This paper presents a centralized multivariable control strategy for the control of the DFIG wind turbine. Model of one-mass wind turbine with DFIG is represented by a third-order model. Model predictive control is applied in order to compensate inaccuracies and measurement noise. The optimization problem is recast as a Quadratic Program (QP) which is highly robust and efficient. In order to bring the problematic voltages as closely as possible to the desired values, multi-step optimization is introduced. The effectiveness of the proposed algorithm is demonstrated for a variable speed wind turbine.