State-Space Evolving Granular Control of Unknown Dynamic Systems

Daniel Leite

Research output: Contribution to journalConference articlepeer-review


We present an approach for data-driven modeling and evolving control of unknown dynamic systems called State-Space Evolving Granular Control. The approach is based on elements of granular computing, discrete state-space systems, and online learning. First, the structure and parameters of a granular model is developed from a stream of state data. The model is formed by information granules comprising first-order difference equations. Partial activation of granules gives global nonlinear approximation capability. The model is supplied with an algorithm that constantly updates the granules toward covering new data; however, keeping memory of previous patterns. A granular controller is derived from the granular model for parallel distributed compensation. Instead of difference equations, the content of a control granule is a gain matrix, which can be redesigned in real-time from the solution of a relaxed locally-valid linear matrix inequality derived from a Lyapunov function and bounded control-input conditions. We have shown asymptotic stabilization of a chaotic map assuming no previous knowledge about the source that produces the stream of data.

Original languageEnglish
JournalCEUR Workshop Proceedings
StatePublished - 2022
Externally publishedYes
Event1st Workshop on Online Learning from Uncertain Data Streams, OLUD 2022 - Padova, Italy
Duration: 18 Jul 2022 → …


  • Chaos
  • Dynamic Systems
  • Evolving Control
  • Granular Computing


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