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The project focuses on developing a machine learning-assisted model order reduction framework for a Self-Balancing Bicycle Robot (SBBR) to enhance control stability while reducing computational complexity. Significant progress has been made in formulating an objective function, generating a dataset of stable parameter combinations, and implementing regression models like Gaussian Process Regression (GPR) and Support Vector Regression (SVR). Preliminary results indicate that while GPR shows high accuracy, SV

Author

manishka.off

Language

EN