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arXiv · 2102.09503

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

Daniel Leite·Pedro Coutinho·Iury Bessa·Murilo Camargos·Luiz Cordovil Junior·Reinaldo Palhares
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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.

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Daniel Leite, Pedro Coutinho, Iury Bessa, Murilo Camargos, Luiz Cordovil Junior, Reinaldo Palhares. 2021-02-18. Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model. https://arxiv.org/abs/2102.09503

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