Modern technologies such as optical satellite communication and electron microscopy must meet strict positioning requirements while remaining economically competitive. This research develops a data-driven calibration and control framework that compensates for imperfections in cost-effective components, enabling savings without sacrificing performance. In this framework, we augment physics-based models with flexible model structures, machine learning, nonlinear identification and learning control. Experiments show large performance improvements across Switched Reluctance Motors, piezo-stepper actuators, and linear Hall sensors. The results indicate that this framework enables substituting cheaper components without loss of precision, or extracting extra performance from high-end hardware, achieving scalable and affordable precision systems.

Spreker: : Max van Meer, Control Engineer at MI-Partners

Company: PhD research at TU/e

Date: November 3 2025

Time: 12:02pm          

Location: Teams

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