SINDy with Control: A Tutorial

Understand

Many dynamical systems of interest are nonlinear, with examples in turbulence, epidemiology, neuroscience, and finance, making them difficult to control using linear approaches.

• Model predictive control (MPC) is a powerful model-based optimization technique that enables the control of such nonlinear systems with constraints.
• However, modern systems often lack computationally tractable models, motivating the use of system identification techniques to learn accurate and efficient models for real-time control.
• In this tutorial article, we review emerging data-driven methods for model discovery and how they are used for nonlinear MPC.