Electro-mechanical oscillations are produced in the machines of an interconnected power network, followed by a disturbance or due to high power transfer through weak tie lines. These oscillations should be damped as quickly as possible to ensure the reliable and stable operation of the network. To damp these oscillations different controllers, based on local or wide area signals, have been the subject of many papers. This paper presents the analysis of the PID of Conventional Power System Stabilizer (PSS) and Fuzzy Logic Controller.

In this paper, a new comparative approach was proposed for reliable controller design. Scientists and engineers are often confronted with the analysis, design, and synthesis of real-life problems. The first step in such studies is the development of a 'mathematical model' which can be considered as a substitute for the real problem. The mathematical model is used here as a plant. Fractional integrals and derivatives have found wide application in the control of dynamical systems when the controlled system and the controller are described by a set of fractional order differential equations. Here the stability and robustness of fractional order system is checked at the different level and it is found that the stability region is large in the complex plane. This large stability region provides the more flexibility for system implementation in the control engineering. Generally, an analytically or experimentally approaches are used for designing the controller. If a fractional order controller design approach used for a given plant then the controlled parameter gives the better result.

In this paper, Teaching-Learning-Based Optimization (TLBO) algorithm is employed for controlling the speed of induction motors using fuzzy sliding mode controller. The proposed control scheme formulates the design of the controller as an optimization problem. First, a sliding mode speed controller with an integral switching surface is designed, in which the acceleration information for speed control is not required. In this case, the upper bound of the lumped uncertainties including the parameter uncertainties and the load disturbance must be available. The importance of this parameter on the system performance is illustrated. Then, the fuzzy sliding mode speed controller is designed to estimate the upper bound of the lumped uncertainties. Finally, TLBO algorithm is adopted to determine the optimal upper bound of the uncertainty. Simulation results are given to demonstrate the superiority of the proposed controller in comparison with the proportional-integrator, traditional sliding mode controller, fuzzy sliding mode controller and adaptive fuzzy sliding mode controller.

The recent outbreak of the COVID-19 disease has just appeared at the end of 2019 that has now become a global pandemic. Analysis of mathematical models in the prediction and control of this pandemic helps to make the right decisions about vaccination, quarantine, and other control measures. In this article, the aim is to analyze the three control measures of edu-cational campaigns, social distancing, and treatment control, that these control measures can reduce the spread of this disease. For this purpose, due to the uncertainty in the model parameters, a sliding mode control law is used. Furthermore, because the model parameters are changing and the upper limit of the parameters that have uncertainty should be known, then an adaptive control is used to estimate the switching gain online. In addition, in order to prevent the chattering phenomenon, the sign func-tion is used in the sliding control law. Also, the obtained properties are expressed and proven analytically. Therefore, initially, the controller is designed assuming certain knowledge of an upper bound of the uncertainty signal. After that, the parameters that have uncertainty in the simulation are obtained by online estimation of the adaptive control. The efficiency and performance of the controller in the absence of the certainty of the model parameters are investigated, and the results show the desired per-formance of this controller. Finally, the performance and efficiency of the controller are evaluated by simulation.