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

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  • 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.

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Correspondence to Tae-Hyoung Kim.

Additional information

Recommended by Associate Editor Soohee Han under the direction of Editor Euntai Kim. This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2016R1D1A1B03935288), the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government - Ministry of Trade Industry and Energy(MOTIE) (No. N0001075), and the Chung-Ang University Excellent Student Scholarship in 2016.

Yoonkyu Hwang received the B.E. and M.S. degrees in mechanical engineering from Chung-Ang University, Korea, in 2015 and 2017, respectively. His research focuses on the use of populationbased searches for artificial intelligence including system identification, robust control and autonomous navigation of mobile robot.

Young-Rae Ko received the B.E. degree in electrical and electronics engineering and the M.S. and Ph.D. degrees in mechanical engineering from Chung-Ang University, Korea, in 2010, 2012, and 2017, respectively. His research interests include hysteresis identification and compensation, system identification, disturbance observer, robust control, and adaptive control.

Youngil Lee received the B.E. and M.S. degrees in mechanical engineering from Chung-Ang University, Korea, in 2013 and 2015, respectively. His research interests include artificial intelligence, robust control, and robotics.

Tae-Hyoung Kim received the B.S. and M.S. degrees in mechanical engineering from Chung-Ang University, Korea, in 1999 and 2001, respectively. He received the Ph.D. degree in informatics from Kyoto University, Japan, in 2006. He is currently an Associate Professor at the School of Mechanical Engineering, Chung-Ang University. His current research interests include robust control, multi-agent system, particle swarm optimization, system identification, model predictive control, iterative learning control and systems biology.

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Hwang, Y., Ko, YR., Lee, Y. et al. Frequency-domain Tuning of Robust Fixed-structure Controllers via Quantum-behaved Particle Swarm Optimizer with Cyclic Neighborhood Topology. Int. J. Control Autom. Syst. 16, 426–436 (2018). https://doi.org/10.1007/s12555-016-0766-3

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  • DOI: https://doi.org/10.1007/s12555-016-0766-3

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