Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model

DOI

10.2991/asum.k.210827.011How to use a DOI?

Keywords

Data Stream, Evolving System, Fuzzy Control, Linear Matrix Inequality

Abstract

We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The method combines elements of evolving intelligence, granular machine learning, and multi-variable control. We propose a State-Space Fuzzy-set-Based evolving Modeling (SS-FBeM) approach. The resulting fuzzy model is structurally and parametrically developed from a data stream with focus on memory and data coverage. The fuzzy controller also evolves, based on the data instances and fuzzy model parameters. Its local gains are redesigned in real-time – whenever the corresponding local fuzzy models change – from the solution of a linear matrix inequality problem derived from a fuzzy Lyapunov function and bounded input conditions. We have shown one-step prediction and asymptotic stabilization of the Henon chaos.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Daniel Leite
AU  - Pedro Coutinho
AU  - Iury Bessa
AU  - Murilo Camargos
AU  - Luiz A. Q. Cordovil Junior.
AU  - Reinaldo Palhares
PY  - 2021
DA  - 2021/08/30
TI  - Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model
BT  - Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
PB  - Atlantis Press
SP  - 80
EP  - 87
SN  - 2589-6644
UR  - https://doi.org/10.2991/asum.k.210827.011
DO  - 10.2991/asum.k.210827.011
ID  - Leite2021
ER  -