Zurück zur Auswahl
Semester | Frühjahrsemester 2025 |
Angebotsmuster | Jedes Semester |
Dozierende |
Nicolas Gerig (nicolas.gerig@unibas.ch)
Murali Krishna Karnam (murali.karnam@unibas.ch) Georg Rauter (georg.rauter@unibas.ch, BeurteilerIn) |
Inhalt | Nowadays, there is large knowledge available about control from a theoretical point of view. However, getting an entire setup working from hardware integration, safety, control, up to the graphical user interface or virtual environment, is seldom taught. Participants will learn about basic differences in various automatization environments such as dSPACE, Matlab xPC Target, Matlab/Simulink, LabVIEW, and TwinCAT3. Within one week, the participants will learn how to integrate motors, sensors, and safety components in a predesigned electric cabinet for automation and control purposes. They will develop an automation application for an automated basket scoring task. In groups up to four, the participants will learn how to integrate different hardware components in a real-time control system (TwinCAT3, Beckhoff). They will learn how to account for software safety for an application involving servo motors. After successful hardware and software safety integration, different control schemes (model based controllers, non-linear controllers, vision-based non-linear controllers, etc. ) will be integrated in Matlab/Simulink. After compilation for TwinCAT3, the controllers will work on an industrial embedded real-time PC. During runtime, the participants will be able adapting controllers-online, record data, and see the influence of different filters. Consequently, the participants will program their own graphical user interface (GUI) in PLC and if there is time using the Human Machine Interface (HMI) from Beckhoff. This GUI can be interfaced with the real-time environment through an Automation Device Specification (ADS), i.e. a field bus interface for TwinCAT3. Finally, the groups can work under guidance and also independently on different control algorithms for successfully automating throwing a ball into a basket. In case there should be time, also machine vision will be demonstrated to close the control loop using real-time machine learning algorithms implemented in PLC. |
Lernziele | Hardware, and software integration in real-time applications. Basic knowledge in applied control (model-based control, non-linear control, cascade control). GUI-programming for real-time applications. Real-time data extraction using computer vision algorithms. |
Teilnahmevoraussetzungen | Basic knowledge in control, automation, computer vision, Matlab/Simulink and Unity programming is of advantage, but not required. Master program in Biomedical Engineering |
Unterrichtssprache | Englisch |
Einsatz digitaler Medien | kein spezifischer Einsatz |
Intervall | Wochentag | Zeit | Raum |
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Bemerkungen |
This course is offered after each semester in February and September Hegenheimermattweg 167b, 4123 Allschwil 10.2. – 14.02.2025 |
Keine Einzeltermine verfügbar, bitte informieren Sie sich direkt bei den Dozierenden.
Module |
Doktorat Biomedizinische Technik: Empfehlungen (Promotionsfach: Biomedizinische Technik) Modul: Image-Guided Therapy (Masterstudium: Biomedical Engineering (Studienbeginn vor 01.08.2023)) Modul: Project Work and Practical Skills (Masterstudium: Biomedical Engineering) |
Prüfung | Lehrveranst.-begleitend |
Hinweise zur Prüfung | Participants will have to record and hand in instruction videos (5 min) on selected topics of the course in small groups and a video that addresses the overall impression on the course. In addition, the participants need to be present at least for 80% of the course. The course is rated as failed or passed. |
An-/Abmeldung zur Prüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
Wiederholungsprüfung | keine Wiederholungsprüfung |
Skala | Pass / Fail |
Belegen bei Nichtbestehen | beliebig wiederholbar |
Zuständige Fakultät | Medizinische Fakultät |
Anbietende Organisationseinheit | Departement Biomedical Engineering (DBE) |