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

Research output: Contribution to journal › Article

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

Access to Document

10.1109/TFUZZ.2018.2883540

Link to publication in Scopus

Fingerprint Dive into the research topics of 'Adaptive Neuro-Fuzzy Control for Discrete-Time non-Affine Nonlinear Systems'. Together they form a unique fingerprint.

Cite this

Gil, P., Oliveira, T., & Palma, L. (Accepted/In press). Adaptive Neuro-Fuzzy Control for Discrete-Time non-Affine Nonlinear Systems. Ieee Transactions On Fuzzy Systems. https://doi.org/10.1109/TFUZZ.2018.2883540

@article{d9a752af6d304db49903c5c1ee039c4b,

title = "Adaptive Neuro-Fuzzy Control for Discrete-Time non-Affine Nonlinear Systems",

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.",

keywords = "Kalman filter, Neuro-Fuzzy Control, Nonlinear time-varying systems, recursive learning, unscented transform",

author = "Paulo Gil and Tiago Oliveira and Luis Palma",

note = "info:eu-repo/grantAgreement/FCT/5876/147324/PT# info:eu-repo/grantAgreement/FCT/5876/147281/PT# Sem PDF conforme despacho.",

year = "2018",

month = "1",

day = "1",

doi = "10.1109/TFUZZ.2018.2883540",

language = "English",

journal = "Ieee Transactions On Fuzzy Systems",

issn = "1063-6706",

publisher = "Institute of Electrical and Electronics Engineers",

}

TY - JOUR

T1 - Adaptive Neuro-Fuzzy Control for Discrete-Time non-Affine Nonlinear Systems

AU - Gil, Paulo

AU - Oliveira, Tiago

AU - Palma, Luis

N1 - info:eu-repo/grantAgreement/FCT/5876/147324/PT# info:eu-repo/grantAgreement/FCT/5876/147281/PT# Sem PDF conforme despacho.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

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.

KW - Kalman filter

KW - Neuro-Fuzzy Control

KW - Nonlinear time-varying systems

KW - recursive learning

KW - unscented transform

UR - http://www.scopus.com/inward/record.url?scp=85057388208&partnerID=8YFLogxK

U2 - 10.1109/TFUZZ.2018.2883540

DO - 10.1109/TFUZZ.2018.2883540

M3 - Article

AN - SCOPUS:85057388208

JO - Ieee Transactions On Fuzzy Systems

JF - Ieee Transactions On Fuzzy Systems

SN - 1063-6706

ER -