Abstract: Model predictive control has attracted much attention in electric drives, but its parameter sensitivity on explicit models poses inherent challenges to the further application. This paper ...

Researchers have developed a method to reduce uncertainty in artificial intelligence (AI) systems by tapping into the power of quantum computers. They say their work represents the first demonstration ...

Abstract: This paper presents a novel approach to practical nonlinear model predictive control (PNMPC) using Kolmogorov–Arnold networks (KANs) as prediction models. KANs are based on the ...

In today’s highly competitive industrial landscape, unplanned downtime is more than just an inconvenience; it is a direct threat to productivity, profitability, and safety. Industries such as oil & ...

Public experiment log using Get Physics Done (GPD) with Codex to explore predictive control of tokamak plasma turbulence and confinement. A physics-based flight simulator for optimizing airbrake ...

The current microgrids are experiencing growing difficulties in voltage stability and operational capacity, particularly with constant power loads (CPLs), leading to negative impedance behavior and ...

Traditional LFC strategies based on fixed-gain PI or PID controllers are inadequate for modern power systems characterized by high renewable penetration and dynamic operating conditions. These ...

This study investigates a novel dual-loop control strategy that combines sliding mode and model predictive controllers to reduce torque ripple in high-performance Brushless Direct Current (BLDC) ...

Every modern enterprise collects terabytes of information across systems — CRM, ERP, social media, IoT — yet only a few turn that data into foresight. The differentiator? Python. According to PwC Data ...

do-mpc is a comprehensive open-source toolbox for robust model predictive control (MPC) and moving horizon estimation (MHE). do-mpc enables the efficient formulation and solution of control and ...

Joseph Alderman et al argue that predictive models in healthcare lack adequate oversight and regulation. They highlight the potential risks to patients and call for improved governance to ensure 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.