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

We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The combines elements evolving intelligence, granular machine learning, multi-variable control. propose State-Space Fuzzy-set-Based Modeling (SS-FBeM) approach. resulting fuzzy model is structurally parametrically developed from data stream with focus on memory coverage. controller also evolves, based the instances parameters. Its local gains are redesigned in real-time -- whenever corresponding models change solution linear matrix inequality problem derived Lyapunov function bounded input conditions. have shown one-step prediction asymptotic stabilization Henon chaos.