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.


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    Titel :

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


    Beteiligte:


    Erscheinungsdatum :

    28.08.2023


    Format / Umfang :

    1386818 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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