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

Paulo Gil; Tiago Oliveira; Luis Palma

doi:10.1109/TFUZZ.2018.2883540

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

Paulo Gil, Tiago Oliveira, Luis Palma

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

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 language	English
Journal	Ieee Transactions On Fuzzy Systems
DOIs	https://doi.org/10.1109/TFUZZ.2018.2883540
Publication status	Accepted/In press - 1 Jan 2018

Keywords

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

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10.1109/TFUZZ.2018.2883540

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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.",

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AB - 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.

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

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Keywords

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