Abstract: The high performance of conventional model predictive control (CMPC) for electric drives depends on the fidelity of the machine’s parametric model. However, the parameters of the interior ...

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Abstract: Model Predictive Control (MPC) is a widely used technique for controlling power converters, but its performance is highly dependent on the appropriate sampling time selection. In ...

1 Faculty of Electrical Technology and Engineering (FTKE), Universiti Teknikal Melaka Malaysia, Melaka, Malaysia. 2 Institut Matematik Kejuruteraan (IMK), Universiti Malaysia Perlis (UniMAP), Perlis, ...

This document provides a detailed explanation of the MATLAB code that demonstrates the application of the Koopman operator theory for controlling a nonlinear system using Model Predictive Control (MPC ...

An international research team has tested a hybrid control technique combining adapted perturb and observe (APO) with model-predictive control to address complex partial shading in solar arrays. The ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In this research work authors have experimentally validated a blend of Machine ...

Results: The MIMIC-IV and eICU-CRD datasets comprised 6129 and 709 patients, respectively, who were included in the analysis. Fifty-four features were selected to construct the predictive model.

This work extends our prior work on the distributed nonlinear model predictive control (NMPC) for navigating a robot fleet following a certain flocking behavior in unknown obstructed environments with ...