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Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model
LEITE, DANIEL FURTADO; COUTINHO, PEDRO HENRIQUE S.; BESSA, IURY V.; CAMARGOS FILHO, M. C. O.; CORDOVIL JÚNIOR, LUIZ ALBERTO QUEIROZ; PALHARES, Reinaldo Martinez
Resumo
Resumo
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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palavras-chave
Palavras-chave
incremental learningnonlinear time-varying systemsunknown system modelmultivariable controllinear matrix inequalitiesfuzzy Lyapunov functionHenon mapchaos controlasymptotic stabilizationone-step-ahead prediction
Status
Published
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