Intelligent digital redesign of t-s fuzzy controller via particle swarm optimization

Chen Chien Hsu*, Shu Han Chu, Yi Hsing Chien

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this paper, a novel method is proposed to solve the complex mathematical model of digital redesign of nonlinear systems which is regarded as difficult to approximate. The paper uses the T-S fuzzy model and particle swarm optimization (PSO) to search for the range of digital-controller parameters and to obtain the optimized digital controller using this algorithm. Due to the difficulty in establishing the discrete model of the interval system and designing the digital controller of the interval system, we have formulated the design problem into an optimization problem of a cost function. First, we process the continuous-time nonlinear systems using the T-S fuzzy model, followed by designing a continuous-time controller using individual rules. The next step is to express all possible linear systems as interval systems and search for the range of digital-controller parameters using PSO to narrow down the search range and conveniently search for the optimal solutions. According to the search range of digital controller parameters, the PSO is used to search for the discrete-time controller based on individual rules, so that the states of the discrete-time model based on the fuzzy model approximate to those of the continuous-time nonlinear systems. Finally, one example is given to prove this method is more accurate than the existing one with faster execution speed.

Original languageEnglish
Pages (from-to)193-205
Number of pages13
JournalInternational Journal of Computational Intelligence in Control
Issue number2
Publication statusPublished - 2020 Jul 1


  • Intelligent digital resdesign
  • Nonlinear system
  • Particle swarm optimization
  • T-S fuzzy model

ASJC Scopus subject areas

  • Biotechnology
  • Computational Mechanics
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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