Dictionary-free Koopman model predictive control with nonlinear input transformation

Understand

This paper introduces a method for data-driven control based on the Koopman operator model predictive control.

• Unlike exiting approaches, the method does not require a dictionary and incorporates a nonlinear input transformation, thereby allowing for more accurate predictions with less ad hoc tuning.
• In addition to this, the method allows for input quantization and exploits symmetries, thereby reducing computational cost, both offline and online.
• Importantly, the method retains convexity of the optimization problem solved within the model predictive control online.