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.
|Journal||CEUR Workshop Proceedings|
|State||Published - 2022|
|Event||1st Workshop on Online Learning from Uncertain Data Streams, OLUD 2022 - Padova, Italy|
Duration: 18 Jul 2022 → …
- Dynamic Systems
- Evolving Control
- Granular Computing