Interactive Jupyter Notebooks for the Control Systems II class at ETH Zürich
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Updated
Mar 20, 2025 - Jupyter Notebook
Interactive Jupyter Notebooks for the Control Systems II class at ETH Zürich
version control for working code for ENG3370 - intro to fdbk sys & ctrls - http://faculty.olin.edu/klundberg/controls/
This repo features my Jupyter notebook implementations of robotics learning and optimization codes, inspired by https://rcfs.ch/.
This repository implements a Kalman Filter to estimate a noisy range measurement. The simulation generates Gaussian noise around an actual range and applies the Kalman filter to smooth the readings. The project demonstrates how Kalman filtering can be used for real-world applications such as sensor fusion, navigation, and signal processing.
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