Abstract: Implementing model predictive control (MPC) in practice faces many subtle but prevalent problems, including modeling errors, solver errors, and actuator faults. In essence, the real control ...

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 ...

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 ...

Google has quietly reworked Gemini‘s usage limits, splitting the shared pool and boosting the individual caps for the Thinking and Pro models. At launch, both models had the same daily quota, meaning ...

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 ...

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.