  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.
