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Summary

This study introduces a novel method for designing robust non-fragile proportional-integral-derivative (PID) controllers for automatic voltage regulators (AVRs). The approach ensures system stability and controller integrity despite uncertainties and implementation errors.

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Computing algorithmsKharitonov’s theoremResiliencyRobustnessUncertain systems

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Area of Science:

  • Control Systems Engineering
  • Electrical Engineering
  • Optimization Algorithms

Background:

  • Ensuring robust stability and controller non-fragility simultaneously is challenging due to plant model uncertainties and controller gain perturbations.
  • Automatic Voltage Regulators (AVRs) require precise control to maintain stable voltage output.

Purpose of the Study:

  • To optimally design a robust non-fragile proportional-integral-derivative (PID) controller for an AVR.
  • To address parametric uncertainties in the plant model and tolerate gain perturbations for simultaneous robust stability and controller non-fragility.

Main Methods:

  • Utilized Kharitonov theorem for interval plant modeling and the Future Search Algorithm (FSA) for PID controller optimization.
  • Employed Routh-Hurwitz stability criteria for Kharitonov's plants and a novel figure-of-demerit (FoD) based performance index.
  • FSA incorporates local and global search methods for fast convergence and prevention of local optima.

Main Results:

  • The FSA-based PID controller effectively handles plant model uncertainties and gain perturbations, ensuring robust stability and non-fragility.
  • The proposed method demonstrated superior performance compared to Artificial Bee Colony (ABC), Teaching-Learning Based Optimization (TLBO), Multi-Objective Extremal Optimization (MOEO), and Non-dominated Sorting Genetic Algorithm II (NSGA II).

Conclusions:

  • The developed FSA-based PID controller design offers a robust and non-fragile solution for AVRs.
  • The technique successfully achieves simultaneous minimization of time-domain specifications while maintaining system stability under uncertainty.