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A novel design of high-sensitive fuzzy PID controller

Authors
Journal
Applied Soft Computing
1568-4946
Publisher
Elsevier
Volume
24
Identifiers
DOI: 10.1016/j.asoc.2014.08.001
Keywords
  • Avr System
  • Ga
  • Pid Controller
  • Rbf-Nn
  • Rule Base
  • Sugeno Fuzzy Logic
Disciplines
  • Computer Science
  • Design
  • Mathematics

Abstract

Abstract A hybrid model is designed by combining the genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic to determine the optimal parameters of a proportional-integral-derivative (PID) controller. Our approach used the rule base of the Sugeno fuzzy system and fuzzy PID controller of the automatic voltage regulator (AVR) to improve the system sensitive response. The rule base is developed by proposing a feature extraction for genetic neural fuzzy PID controller through integrating the GA with radial basis function neural network. The GNFPID controller is found to possess excellent features of easy implementation, stable convergence characteristic, good computational efficiency and high-quality solution. Our simulation provides high sensitive response (∼0.005s) of an AVR system compared to the real-code genetic algorithm (RGA), a linear-quadratic regulator (LQR) method and GA. We assert that GNFPID is highly efficient and robust in improving the sensitive response of an AVR system.

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