Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model
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.
Cite
@article{arxiv.2102.09503,
title = {Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model},
author = {Daniel Leite and Pedro Coutinho and Iury Bessa and Murilo Camargos and Luiz Cordovil Junior and Reinaldo Palhares},
journal= {arXiv preprint arXiv:2102.09503},
year = {2021}
}
Comments
8 pages, 6 figures, IFSA-EUSFLAT 2021