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In this paper, a Fuzzy proportional–integral–derivative (Fuzzy PID) controller design is presented to improve the automatic voltage regulator (AVR) transient characteristics and increase the robustness of the AVR. Fuzzy PID controller parameters are determined by a genetic algorithm (GA)-based optimization method using a novel multi-objective function. The multi-objective function, which is important for tuning the controller parameters, obtains the optimal solution using the Integrated Time multiplied Absolute Error (ITAE) criterion and the peak value of the output response. The proposed method is tested on two AVR models with different parameters and compared with studies in the literature. It is observed that the proposed method improves the AVR transient response properties and is also robust to parameter changes.

This paper presents a controller design method using lead and lag controllers for fractional-order control systems. In the presented method, it is aimed to minimize the error in the control system and to obtain controller parameters parametrically. The error occurring in the system can be minimized by integral performance criteria. The lead and lag controllers have three parameters that need to be calculated. These parameters can be determined by the simulation model created in the Matlab environment. In this study, the fractional-order system in the model was performed using Matsuda’s fourth-order integer approximation. In the optimization model, the error is minimized by using the integral performance criteria, and the controller parameters are obtained for the minimum error values. The results show that the presented method gives good step responses for lead and lag controllers.

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In the process control industry, it is arduous to control some integrating or unstable processes since they involve time delays and have an inverse response. Conventional controllers such as PID cannot provide sufficient control performance alone in the control of these systems. This article proposes a control algorithm based on an I-PD-based Smith predictor for the control of time-delayed integrating or unstable inverse processes. The controller parameters are tuned by using the Equilibrium optimizer (EO) algorithm, which is presented in the literature in 2020, in the proposed control approach. The EO algorithm aims to determine the optimal controller parameters by minimizing the error and control signal using a multiobjective function based on ITAE performance criterion. Thus, the controller parameters that will provide the set-point tracking and disturbance rejection control most properly can be determined. Simulation studies are conducted based on different process structures to evaluate the performance of the proposed method. The proposed method is compared with studies from the literature in terms of set-point tracking, parameter uncertainties, control signals, and disturbance rejection. It is seen that the transient responses and disturbance rejection of the time-delayed and inverse response integrating or unstable processes are improved with the proposed method.INDEX TERMS Equilibrium optimizer algorithm, I-PD controller design, inverse response, non-minimum phase system, Smith predictor, and time delay systems.