Multi-objective non dominated sorting genetic algorithm-II optimized PID controller for automatic voltage regulator systems

2018 ◽  
Vol 35 (5) ◽  
pp. 4971-4975 ◽  
Author(s):  
Narendra Kumar Yegireddy ◽  
Sidhartha Panda ◽  
T. Papinaidu ◽  
K. Praveen kumar Yadav
Author(s):  
Tufan Dogruer ◽  
Mehmet Serhat Can

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.


2018 ◽  
Vol 41 (6) ◽  
pp. 1761-1771 ◽  
Author(s):  
Baran Hekimoğlu

A novel design method, sine-cosine algorithm (SCA) is presented in this paper to determine optimum proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system. The proposed approach is a simple yet effective algorithm that has balanced exploration and exploitation capabilities to search the solutions space effectively to find the best result. The simplicity of the algorithm provides fast and high-quality tuning of optimum PID controller parameters. The proposed SCA-PID controller is validated by using a time domain performance index. The proposed method was found efficient and robust in improving the transient response of AVR system compared with the PID controllers based on Ziegler-Nichols (ZN), differential evolution (DE), artificial bee colony (ABC) and bio-geography-based optimization (BBO) tuning methods.


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