Design and performance analysis of PID controller for an AVR system using multi-objective non-dominated shorting genetic algorithm-II

2014 ◽  
Author(s):  
Narendra Kumar Yegireddy ◽  
Sidhartha Panda
Keyword(s):  
Genetic Algorithm ◽  
Performance Analysis ◽  
Pid Controller ◽  
Multi Objective ◽  
Avr System ◽  
And Performance

Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm

2022 ◽  
pp. 014233122110667
Author(s):  
Tufan Dogruer ◽  
Mehmet Serhat Can
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Pid Controller ◽  
Robustness Analysis ◽  
Optimal Solution ◽  
Voltage Regulator ◽  
Fuzzy Pid ◽  
Multi Objective ◽  
Fuzzy Pid Controller ◽  
Automatic Voltage Regulator

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.

Research on a Multi-Objective Genetic Algorithm for Rational Agent Learning Model

2011 ◽  
Vol 58-60 ◽  
pp. 1232-1239
Author(s):  
Ming Li ◽  
Zhen Hong Xiao ◽  
Zan Fu Xie ◽  
Xiao Yun Mo
Keyword(s):  
Genetic Algorithm ◽  
Global Convergence ◽  
Dynamic Environment ◽  
Learning Model ◽  
Dynamic Learning ◽  
Learning Capability ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Wide Perspective ◽  
And Performance

As a software component which is capable of learning in an autonomous way, software agent should have the capability of learning in a dynamic environment. Genetic Algorithm has a wide perspective in the machine learning because of its unique characteristic (e.g. dynamic adaptability, self-organization, global convergence and robustness). But when applying GA to agent’s dynamic learning model, it encounters a series of problem. In this paper, a Modifided Multi-Objective Genetic Algorithm(MMOGA) will be introduced to solve these problems. Finally, an Agent’s Dynamic learning model based on a MMOGA which has the flexible dynamic learning capability, better global convergence and performance, will be introduced.

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