state-space-linearization

To linearize nonlinear dynamics, you compute the Jacobian matrices A and B by evaluating the partial derivatives of your system equations dx/dt = f(x, u) at a chosen reference operating point. This yields a local linear state-space model suitable for controller design.

Euler discretization offers a simple approximation for converting continuous-time state-space models to discrete-time, while the exact matrix exponential method provides precise step propagation. Both options are supported to generate a usable discrete linear model from your Jacobian matrices.

Yes, Jacobian-based linearization is suitable for aerospace, robotics, and run-to-run (R2R) control tasks. It applies to any system described by dx/dt = f(x, u) where you need to obtain local linear state-space matrices for analysis and controller design.

You obtain A and B matrices by computing the Jacobian of your nonlinear dynamics with respect to the states and inputs, respectively, evaluated at the operating point. The resulting linear model also includes guidance for performing stability checks.

You need to linearize a nonlinear system when your controller design or stability analysis requires a linear state-space model. Linearization produces local approximations A and B that are valid around a specific operating point, enabling standard linear control techniques.