Sensor Fusion for Nanopositioning

Sensors can be used to measure the position of an object. In the present thesis the effects which limit the usage of sensors in high dynamic positioning applications on a nanometer level are discussed. Various sensor principles and their properties are investigated and compared. Sensors based on the measurement of i.a. magnetic fields, illumination, or even strain are characterized, as well as their range, bandwidth, resolution, linearity and disturbance rejection is determined.
It will be shown that the simultaneous use of multiple sensors and the specific combination of sensors’ data (fusion) enables a higher performance primarily in terms of resolution and dynamics. Several techniques for the fusion are discussed under consideration of various aspects, however the ultimate aim of sensor fusion is similar.
The methods of feedforward control, complementary filtering, Kalman filtering and optimal filtering (robust control) are developed and verified on practical problems in position sensor systems. To treat various challenges in sensor filtering and sensor fusion a methodological approach, containing separable steps of
• problem formulation with well-defined prerequisits and simplifications,
• theory discussion with approach to find a solution,
• analytical proof or reasoning by statistical values out of numerical simulations,
• experiment design, and
• verification on a real time platform are realized.