Nonlinear State-Space Models

Nonlinear state-space models are mathematical tools used to describe how a system changes over time, especially when those changes don’t follow straight-line (linear) rules. They represent the system’s current condition with a set of variables called "states," which evolve based on internal dynamics and inputs, often in complex ways. These models are used in fields like control engineering and robotics to predict and analyze systems such as weather patterns, biological processes, or aircraft behavior, capturing their real-world complexity more accurately than linear models.