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.
Model Predictive Control Utilizing Machine Learning Models within a Pinball-Based, Cyber-Physical Testbed
2023-08-28
1386818 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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