Model Predictive Control Utilizing Machine Learning Models within a Pinball-Based, Cyber-Physical Testbed

We examine the shot aiming problem within a physical pinball machine using model predictive control methods and machine learning based system models. A switched mode system model is developed and trained using data collected from an infrared beam-break sensor array that allows the estimation and prediction of future ball states. The trained model is then used within a model predictive controller to successfully aim shots within a physical pinball machine. The experimental results show that the controller performs with sufficient accuracy to hit standard pinball targets found in commercial pinball machines.