Frequency-domain Tuning of Robust Fixed-structure Controllers via Quantum-behaved Particle Swarm Optimizer with Cyclic Neighborhood Topology

  • Yoonkyu Hwang
  • Young-Rae Ko
  • Youngil Lee
  • Tae-Hyoung Kim
Regular Paper Control Theory and Applications
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Abstract

This paper presents a constrained particle swarm optimization (PSO) algorithm with a cyclic neighborhood topology inspired by the quantum behavior of particles, and describes its application to the frequency-domain tuning of robust fixed-structure controllers. Two main methodologies for improving the exploration and exploitation performance of the PSO framework are described. First, a PSO scheme with a neighborhood structure based on a cyclic network topology is presented. This scheme enhances the exploration ability of the swarm and effectively reduces the probability of premature convergence to local optima. Second, the above PSO scheme is hybridized using a distributed quantum-principle-based offspring creation mechanism. Such a hybridized PSO framework enables neighboring particles to concentrate the search around the region covered by those particles to refine the candidate solution. A frequency-domain tuning method for fixed-structure controllers is then demonstrated. This method guarantees certain preassigned performance specifications based on the developed PSO technique. A typical numerical example is considered, and the results clearly demonstrate that the proposed PSO scheme provides a novel and powerful impetus with remarkable reliability for robust fixed-structure controller syntheses. Further, an experiment was conducted on a magnetic levitation system to compare the proposed strategy with a well-known frequency-domain tuning method implemented in the MATLAB tool for Structured H∞ Synthesis. The comparative experimental results validate the effectiveness of the proposed tuning strategy in practical applications.

Keywords

Constrained optimization control system synthesis fixed-structure controller particle swarm optimization robust control 
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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yoonkyu Hwang
    • 1
  • Young-Rae Ko
    • 1
  • Youngil Lee
    • 1
  • Tae-Hyoung Kim
    • 1
  1. 1.Department of Mechanical Engineering, College of EngineeringChung-Ang UniversitySeoulKorea

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