| KEYWORD |
Experimental implementation of Robust Adaptive Model Predictive Control
Reference persons ELISA CAPELLO
External reference persons FALIERO FABIO, fabio.faliero@polito.it
Research Groups 08- Dinamica, controllo e simulazione del volo
Description This thesis explores Robust Adaptive Model Predictive Control (RAMPC) for a differential drive robot (DDR) using data-driven linear models. The research is structured in two phases. The first phase involves deriving a linear data-driven model of the DDR from experimental data, focusing on capturing parametric uncertainties that affect the model's accuracy. These uncertainties will be integrated into the model to formulate the RAMPC problem. The second phase entails the development and implementation of the RAMPC algorithm. The candidate is required to write code in Python or C++ and integrate it with ROS 2 for real-time control. The RAMPC algorithm will adapt the model continuously based on sensor feedback, ensuring robust performance despite uncertainties and external disturbances. The implementation will be validated on a physical DDR, demonstrating the RAMPC algorithm's ability to track some piecewise constant reference signals.
Required skills Knowledge of Matlab Simulink, Basic Knowledge of Python and C++, Basic Knowledge of Control Systems
Deadline 11/10/2025
PROPONI LA TUA CANDIDATURA