Data-Driven Model Predictive Control of a Nonlinear Ball-on-a- Wheel System

The topic of this paper is the design of a data-driven model predictive controller for a nonlinear ball-on-a-wheel system. A Koopman predictor is identified using simu-lation data of a ball-on-a-wheel system model. The challenge lies in using data, which is characterized by the instability of the given system. This limits the time horizon during which meaningful time series can be extracted. Within this scenario, the predictor is included in a model predictive control scheme. This controller successfully tracks a specified non-flat output trajectory subject to input and output constraints. Finally, the prediction quality of the Koopman predictor is compared with a linear predictor based on dynamic mode decomposition with control.