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Low order robust controller design for preserving Hinfinity performance: genetic algorithm approach.

Ching-Hung Lee1

  • 1Department of Electrical Engineering, Yuan Ze University, No. 135, Yuan-tung Road, Chung-li, Taoyuan 320, Taiwan, Republic of China. chlee@saturn.yzu.edu.tw

ISA Transactions
|November 13, 2004
PubMed
Summary

This study develops low-order robust controllers using a genetic algorithm, bridging the gap between high-order Infinity controller theory and practical engineering applications. The method ensures performance comparable to optimal controllers.

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Area of Science:

  • Control Systems Engineering
  • Computational Intelligence
  • Robust Control Theory

Background:

  • Traditional Infinity controller design often results in high-order controllers, limiting practical engineering applications.
  • A significant gap exists between theoretical Infinity control methods and their real-world implementation due to controller complexity.
  • Robust control is essential for systems operating under uncertainty, demanding controllers that maintain stability and performance.

Purpose of the Study:

  • To design low-order robust controllers that achieve performance comparable to high-order Infinity optimal controllers.
  • To address the practical limitations of existing Infinity controller design methods by reducing controller order.
  • To utilize a real-code genetic algorithm for optimizing low-order controller parameters within a defined performance index.

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Main Methods:

  • Design of an initial Infinity optimal controller using Glover and Doyle's results to establish a performance benchmark (gamma).
  • Selection of a low-order controller structure, such as a first-order or PID controller, with adjustable parameters.
  • Application of a real-code genetic algorithm to tune the parameters of the low-order controller, aiming to preserve the performance index gamma.

Main Results:

  • The proposed method successfully designs low-order controllers that maintain the performance index of higher-order Infinity controllers.
  • Computational simulations demonstrate the effectiveness and feasibility of the genetic algorithm-based approach.
  • The developed controllers offer a practical alternative for robust control applications where complexity is a constraint.

Conclusions:

  • The real-code genetic algorithm is an effective tool for designing practical, low-order robust controllers.
  • This approach successfully bridges the theory-practice gap in Infinity control by reducing controller order while maintaining performance.
  • The findings support the wider adoption of robust control techniques in engineering practice through simplified controller designs.