On-line adaptive interval type-2 fuzzy controller design via stable SPSA learning mechanism

Ching Hung Lee*, Feng Yu Chang, Chih Min Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

This paper proposes an interval type-2 Takagi- Sugeno-Kang fuzzy neural system (IT2TFNS) to develop an on-line adaptive controller using stable simultaneous perturbation stochastic approximation (SPSA) algorithm. The proposed IT2TFNS realizes an interval type-2 TSK fuzzy logic system formed by the neural network structure. Differ from the most of interval type-2 fuzzy systems, the type-reduction of the proposed IT2TFNS is embedded in the network by using uncertainty bounds method such that the time-consuming Karnik-Mendel (KM) algorithm is replaced. The proposed stable SPSA algorithm provides the gradient free property and faster convergence. However, the stable SPSA algorithm inherently has the problem for on-line adaptive control. Hence, in order to achieve the on-line result, we utilize the sliding surface to develop a new on-line adaptive control scheme. In addition, the corresponding stable learning is derived by Lyapunov theorem which guarantees the convergence and stability of the closed-loop systems. Simulation and comparison results are shown to demonstrate the performance and effectiveness of our approach.

Original languageEnglish
Pages (from-to)489-500
Number of pages12
JournalInternational Journal of Fuzzy Systems
Volume14
Issue number4
StatePublished - Dec 2012

Keywords

  • Interval type-2 fuzzy neural system
  • Lyapunov theorem
  • On-line control
  • Simultaneous perturbation stochastic approximation algorithm
  • Uncertainty bounds

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