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High-performance control, precise torque regulation, and minimal stator current total harmonic distortion (THD) of induction motor drives (IMDs) have always been considered an industrial concern. The conventional finite control set model predictive control (FCS-MPC) strategy suffers from high computational complication, increased torque ripple, and stator current THD, which is employed to drive the voltage source inverter (VSI) based IMD. This paper proposes an optimized method of selecting prediction vectors to minimize the computational cost of the traditional FCS-MPC for a two-level VSI-based IMD. By minimizing the number of prediction vectors from six to three utilizing the proposed strategy, the cost function is assessed for only four vectors. The proposed improved model predictive control (MPC) is based on finite control set predictive torque control (FCS-PTC). The proposed improved MPC strategy also prioritizes the selection of the zero vector by avoiding the phase arm that carr ....
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Abstract This paper presents a continuous control set model predictive control (MPC) based advanced switching sequence (SS) for the single-phase three-level grid connected neutral point clamp converters. The SSs are developed considering the tracking performance of the current feed to the utility grid, balancing the dc-bus capacitors voltages as well as the switching loss in the semiconductor devices. In the proposed strategy, the number of pole state change (SC) per control cycle is only 2, while in the conventional optimum switching sequence and the multiple voltage vector (VV) based MPC (pulse sequence similar to the conventional space vector pulse width modulation technique), the number of pole SC is 4. The reduction of SC in each control cycle greatly reduces the switching loss of the power devices. Moreover, the dc-bus capacitors voltages are balanced using the redundancy nature of the VVs that further facilitates a weighting factor less design of the proposed technique. ....
As part of a personal push to learn more about non-linear control, I have been playing around with a pretty powerful method known as trajectory optimization. Once the base code is set up, it’s fairly easy to go apply to various systems. Here’s a fun example of it being run on a drone: Drone performing flips! While waiting for Starship SN15 to launch (it was in fact scrubbed, sad), I decided to pull together an estimate for some of it’s dynamics and see if I could get my toy 2D simulation to perform that epic flip and land itself. To my excitement, after some finessing, it worked quite well. But what really surprised me was when I played the output side by side with actual landing footage: it lined up very very well. I wish I had gotten a live reaction of when this happened, I may or may not have jumped out of my chair. The whole program and optimization was written without any reference to the video or other explicit timing information. ....