Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...
Model predictive control (MPC) is one of the few control frameworks allowing to systematically integrate input and/or state constraints to realize safe control. Nonetheless, traditional MPC requires a ...
In 1930, a young physicist named Carl D. Anderson was tasked by his mentor with measuring the energies of cosmic ...
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 ...
Safety Leadership and Safety Performance: An integrative Model for Ghanaian Construction Industry ()
Safety Leadership, Worker Behaviour, Safety Performance, Integrative Model, Ghanaian Construction Industry Share and Cite: ...
Abstract: This paper proposes a model predictive control method based on a dynamic event-triggered strategy for high-precision formation control of spacecraft formations using continuous low thrust.
What would a Tesla be without controversy and split opinions? The Tesla Model Y’s midcycle refresh brought significant enough changes to earn it a spot in our 2026 SUV of the Year competition. The ...
They look, move and even smell like the kind of furry Everglades marsh rabbit a Burmese python would love to eat. But these bunnies are robots meant to lure the giant invasive snakes out of their ...
Introduction: This work presents an approach to collision avoidance in multi-agent systems (MAS) by integrating Conflict-Based Search (CBS) with Model Predictive Control (MPC), referred to as Conflict ...
The 2025 Florida Python Challenge, a competition to remove invasive Burmese pythons, runs from July 11 to 20. The challenge aims to raise public awareness and encourage reporting of python sightings.
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.
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