Sensor Fusion for Nanopositioning

Inhaltsverzeichnis (Table of Contents)

• Acknowledgement
• Abstract
• Zusammenfassung
• 1. Introduction
• 1.1. Sensor Principles
• 1.2. Sensor Fusion
• 2. Sensor Characterization
• 2.1. Position Measurement
• 2.1.1. Magnetic Fields
• 2.1.2. Illumination
• 2.1.3. Strain
• 2.2. Sensor Noise
• 3. Sensor Fusion Techniques
• 3.1. Feedforward Control
• 3.2. Complementary Filtering
• 3.3. Kalman Filtering
• 3.4. Optimal Filtering
• 4. Applications
• 4.1. Nanopositioning Systems
• 4.2. Atomic Force Microscopy
• 5. Conclusion

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This master thesis explores the limitations of sensors in high-dynamic nanopositioning applications and proposes solutions through sensor fusion techniques. The work aims to demonstrate the advantages of combining multiple sensor data for achieving higher resolution and bandwidth in position control. Key themes explored in the thesis include:

• Sensor principles and their limitations in nanopositioning.
• Sensor noise analysis and its impact on performance.
• Methods for sensor data fusion, including feedforward control, complementary filtering, Kalman filtering, and optimal filtering.
• Practical applications of sensor fusion in nanopositioning systems and atomic force microscopy.
• A methodological approach for problem formulation, theoretical analysis, and verification on real-time platforms.

Zusammenfassung der Kapitel (Chapter Summaries)

• Chapter 1: Introduction This chapter introduces the concept of sensor fusion and its relevance in high-dynamic nanopositioning applications. It outlines the limitations of individual sensors and highlights the benefits of combining data from multiple sensors.
• Chapter 2: Sensor Characterization This chapter focuses on the characterization of different sensor principles used in nanopositioning, including magnetic fields, illumination, and strain. It examines the properties of each sensor type, such as range, bandwidth, resolution, linearity, and disturbance rejection.
• Chapter 3: Sensor Fusion Techniques This chapter presents various sensor fusion techniques, including feedforward control, complementary filtering, Kalman filtering, and optimal filtering. It discusses the advantages and limitations of each technique and explores their application in practical problems.
• Chapter 4: Applications This chapter explores the practical applications of sensor fusion in nanopositioning systems and atomic force microscopy. It demonstrates how sensor fusion can improve performance and address real-world challenges in these fields.

Schlüsselwörter (Keywords)

The core themes and concepts explored in this work include sensor fusion, nanopositioning, high dynamics, high resolution, sensor noise, Kalman filter, H2-optimal filter, and H∞-optimal filter. This thesis examines the application of these concepts in achieving high-precision position control for nano-scale applications, focusing on the advantages and limitations of various sensor fusion techniques.