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This paper introduces a method for data-driven control based on the Koopmanoperator model predictive control. Unlike exiting approaches, the method doesnot require a dictionary and incorporates a nonlinear input transformation,thereby allowing for more accurate predictions with less ad hoc tuning. Inaddition to this, the method allows for input quantization and exploitssymmetries, thereby reducing computational cost, both offline and online.Importantly, the method retains convexity of the optimization problem solvedwithin the model predictive control online. Numerical examples demonstratesuperior performance compared to existing methods as well as the capacity tolearn discontinuous lifting functions.

Dictionary-free Koopman model predictive control with nonlinear input transformation