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  Common Gate (CG) topologies are commonly used as the first stage in Transimpedance Amplifiers (TIA), due to their low input resistance. But, this structure is not solely used as a TIA and comes with other topologies such as differential amplifiers or negative resistances and capacitances. This paper deals with analyzing the effect of adding an active feedback network to a common gate topology. Generally, the feedback network is used to reduce the input resistance of the CGs topology, but in this paper it is shown that an active feedback network, which occupies a small area, not only reduces further the input resistance of CG topologies, but also forms an active inductive behavior, which can be used to resonate with the large parasitic capacitance of the photodiode and hence obtain a wide bandwidth. Mathematical analysis is done in this paper to prove the existence of this active inductor, which is also proved in the simulations. Finally, it is shown that this stage alongside its active feedback can be used as a high-speed and low-power transimpedance amplifier for optical communication applications.

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The paper presents an optimal and coordinated power oscillation damper based on a wind turbine and power system stabilizer (PSS) to maintain the power system stability and damp inter-area oscillations. The optimal and coordinated design of the PSS located at the generator site and the damper which was installed in the control section of the doubly-fed induction generator (DFIG) is defined as an optimization problem and simulations have been performed in MATLAB software environment. To determine optimal coefficients of the PSS and damper, the metaheuristic salp swarm optimization (SSA) algorithm was employed with an objective function that aimed to minimize the error caused by frequency deviations of two areas. Due to the use of wide-area measurement systems (WAMS) in the proposed damper to enhance controllability and observability of most of the oscillation modes, time delays resultant from the WAMS was also taken into account. Additionally, uncertainties of wind intermittency and time delay of WAMS were calculated probabilistically. The suggested method was applied to a six-machine two-area power system with a wind farm. The obtained simulation results highlighted and validated the superior performance and stability of the power system as a result of using the proposed method.

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In this paper, a control input based on terminal sliding mode control is provided for a mobile robot with four Mecanum wheels to move in a predetermined path and convergence into the path in a fixed-time. First, according to the robot structure, a dynamic model of the robot is presented. The dynamic model follows a nonlinear second-order equation. Based on terminal sliding mode control, a nonlinear sliding surface which is a function of position error vector is defined and then the control input is designed based on this sliding surface. Using the Lyapunov theorem, it has been proven that, using this control input, the robot converges to the predetermined path at a fixed time. The convergence time is a function of the constants defined in the control input. Finally, the simulation is presented based on the control input and the results are shown.

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Electromagnetic suspension technology has been developed in recent years due to advantages such as no contact and reduced friction. Of course, ensuring efficiency in these systems requires precise control of the position of the suspended object. Therefore, electromagnetic suspension is considered as a process by control engineers. The dynamics of electromagnetic suspension systems is nonlinear and also include model and parametric uncertainties such as the weight of the suspended object. In this paper, a finite time nonlinear hybrid method is used to stabilize the electromagnetic suspension system. Proof of finite time stability of the proposed method is performed using Lyapunov theory and a relation for calculating the convergence time depends on the controller gains is presented. To ensure the finite time convergence of the system state and output variables, the backstepping algorithm is used and in each step, the finite-time convergence theory is used. The controller designed in this paper is compared with the backsteping method and the superiority of the proposed method in various simulations is shown.

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The control of unmanned aerial vehicles is a challenging problem due to their lightweight and intense coupling between longitudinal and lateral motion. Considering this issue, in this article, an automatic landing system for a fixed-wing unmanned aircraft exposed to wind disturbances and parametric uncertainties is designed using the backstepping algorithm and the disturbance observer-based sliding mode control. Two controllers are designed based on the backstepping algorithm and sliding mode control to stabilize the attitude angles. The longitudinal speed controller uses the sliding mode technique to maintain the total speed relative to the ground at a constant desired value in all landing phases. A nonlinear disturbance-observer is considered in the sliding mode controller structure to estimate wind disturbance and parametric uncertainty. The new robust automatic landing system is software implemented, and its performance is investigated by several numerical simulations; Lateral deviation relative to the runway is eliminated while the unmanned aerial vehicle maintains its desired trajectory slope angle in all phases of the landing at the desired value. Therefore, the results of numerical simulations prove that the new control structure is stable and robust against different initial conditions, different types of wind disturbances (wind shear and discrete gust), and parametric uncertainty.

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In this article, an efficient method to minimize energy losses is presented. The proposed method uses intermittent load conditions over a future time interval instead of an instantaneous network condition. This method obtains the optimal condition during the given period according to the current value of the condition. A given time interval is divided into many smaller subintervals. By increasing the number of subintervals or load profiles, the dimensions of the problem increase, for which an optimal value must be obtained. In this method, the variables are divided into the group of continuous and discrete control variables. While only continuous control variables are allowed to change in each sub-interval, continuous and discrete variables are set at the beginning of each time interval. This problem is solved by using the GBD general bend decomposition method. Using this method, the load conditions for each subinterval in the NLP subproblem are solved. Then, the results of the NLP subproblem are used in the main subproblem. As shown in the simulation results, the proposed method not only improves the voltage profile but also reduces the total energy wasted in the desired period.