Michael Hesse successfully completed his doctorate on the topic of “Interactive Commissioning of Control Applications for partially known dynamic Systems using Gaussian Process Regression”. The first supervisor and DART follow-up group leader was Dr Julia Timmermann, the chair of the doctoral committee was Prof. Dr Ansgar Trächtler.
Abstract:
The commissioning of control systems ensures that a mechatronic system functions properly and meets the requirements. Model-based design is based on a precise simulation model. However, this classic approach is often impractical for complex systems, as analytical modeling is too complicated and time-consuming. This research gap is addressed by methods that enable efficient and safe commissioning. These methods combine control engineering and reinforcement learning and use existing knowledge about the control task to make corrections based on measurement data and probabilistic Gaussian process regression. The prior knowledge can be available as a partially known physical model or as a control function.
Application examples include the ultrasonic wire bonding process, various pendulum systems and a hexapod. An adapted Bayesian optimization is used to identify a control parameterization for ultrasonic bonding. In addition, a hybrid optimal control for the double pendulum on a cart is developed and successfully validated. A hybrid state linearization is formulated for a hexapod for vehicle axle testing and a proof of concept is provided in a simulation. Compliance with technical framework conditions and stable system behavior are ensured by probabilistic predictions. An increase in efficiency and quality is achieved in all use cases.
