Adaptive Neuro-Fuzzy Control for Discrete-Time non-Affine Nonlinear Systems

Research output: Contribution to journalArticle

Abstract

The problem of non-affine time-varying nonlinear control systems is addressed in this work through an adaptive state-space neuro-fuzzy control scheme. It combines a eight-layered neuro-fuzzy model to approximate non-affine nonlinear systems' dynamics with a state feedback quadratic stabilising controller. Both the neuro-fuzzy model and controller are updated online within a constrained unscented Kalman filter framework. The proposed generalised state-space neuro-fuzzy model is shown to be an universal approximator, and stability conditions derived for time-varying closed loop systems. Results from a benchmark MIMO system demonstrate the effectiveness of the proposed approach.

Original languageEnglish
JournalIeee Transactions On Fuzzy Systems
DOIs
Publication statusAccepted/In press - 1 Jan 2018

Keywords

  • Kalman filter
  • Neuro-Fuzzy Control
  • Nonlinear time-varying systems
  • recursive learning
  • unscented transform

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