Direct adaptive fuzzy-neural control with state observer and supervisory controller for unknown nonlinear dynamical systems

Chi-Hsu Wang*, Han Leih Liu, Tsung Chih Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

233 Scopus citations

Abstract

In this paper, an observer-based direct adaptive fuzzy-neural network (FNN) controller with supervisory mode for a certain class of high order unknown nonlinear dynamical system is presented. The direct adaptive control (DAC) has the advantage of less design effort by not using FNN to model the plant. By using an observer-based output feedback control law and adaptive law, the free parameters of the adaptive FNN controller can be tuned on-line based on the Lyapunov synthesis approach. A supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be de-activated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Simulation results also show that our initial control effort is much less than those in previous works, while preserving the tracking performance.

Original languageEnglish
Pages (from-to)39-49
Number of pages11
JournalIEEE Transactions on Fuzzy Systems
Volume10
Issue number1
DOIs
StatePublished - 1 Feb 2002

Keywords

  • Adaptive control
  • Fuzzy-neural-networks (FNNs)
  • Nonlinear systems
  • Supervisory control

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