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A platform that enables users to perform private benchmarking of machine learning models. The platform facilitates the evaluation of models based on different trust levels between the model owners and the dataset owners.
A wrapped package for Data-enabled predictive control (DeePC) implementation. Including DeePC and Robust DeePC design with multiple objective functions.
This study explores using RL for CSTR control, assessing effectiveness vs. traditional controllers (PID, MPC). RL learns optimal behavior to maximize rewards, outperforming NMPC with exploration and lower computational load.
Official implementation for the paper "CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design" accepted by L4DC 2024. CoVO-MPC is an optimal sampling-based MPC algorithm.
This project demonstrates the deployment of a Model Predictive Controller to balance a ball rolling on a tiltabel platform. OpenCV is employed to get the postional feedback of the ball from the platform for closed-loop control.