Neural-network-based optimal fuzzy controller design for nonlinear systems

Shinq Jen Wu*, Hsin Han Chiang, Han Tsung Lin, Tsu Tian Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


A neural-learning fuzzy technique is proposed for T-S fuzzy-model identification of model-free physical systems. Further, an algorithm with a defined modelling index is proposed to integrate and to guarantee that the proposed neural-based optimal fuzzy controller can stabilize physical systems; the modelling index is defined to denote the modelling-error evolution, and to ensure that the training data for neural learning can describe the physical system behavior very well; the algorithm, which integrates the neural-based fuzzy modelling and optimal fuzzy controlling process, can implement off-line modelling and on-line optimal control for model-free physical systems. The neural-fuzzy inference network is a self-organizing inference system to learn fuzzy membership functions and fuzzy-subsystems' parameters as data feeding in. Based on the generated T-S fuzzy models for the continuous mass-spring-damper system and Chua's chaotic circuit, discrete-time model car system and articulated vehicle, their corresponding fuzzy controllers are formulated from both local-concept and global-concept fuzzy approach, respectively. The simulation results demonstrate the performance of the proposed neural-based fuzzy modelling technique and of the integrated algorithm of neural-based optimal fuzzy control structure.

Original languageEnglish
Pages (from-to)182-207
Number of pages26
JournalFuzzy Sets and Systems
Issue number2
Publication statusPublished - 2005 Sept 1
Externally publishedYes


  • Affine T-S fuzzy system
  • Exponentially stable
  • Linear T-S fuzzy system
  • Modelling index
  • Riccati equation

ASJC Scopus subject areas

  • Logic
  • Artificial Intelligence


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