Results 91 to 100 of about 9,285,453 (352)

Speed Control of Load Torque Feedforward Compensation Based on Linear Active Disturbance Rejection for Five-Phase PMSM

IEEE Access, 2019
This study focuses on the speed control problem of a five-phase permanent magnet synchronous motor (PMSM) in the presence of a variable load torque and unknown model parameters.
Zhi Kuang, Bochao Du, Shumei Cui, C. C. Chan   +3 more
doaj   +1 more source

Feedforward control for oscillatory signal tracking using Hilbert transform

, 2019
This paper presents feedforward control for oscillatory signal tracking using Hilbert transform. The proposed controller utilizes Hilbert transform and analysis techniques to determine the signal\u27s amplitude, frequency, and phase.
Asbj\uf6rnsson, Gauti, Kihlman, Henrik,, Erdem, Ilker,, Asbjörnsson, Gauti,, Kihlman, Henrik, Erdem, Ilker   +5 more
core   +1 more source

Based on the Feedforward Inputs Obtained by the Intelligent Algorithm the Moving Mirror Control System of the Fourier Transform Spectrometer

, 2023
A moving mirror control system of the Fourier transform spectrometer (FTS) based on the feedforward inputs obtained by the intelligent algorithm is proposed in this paper. Feedforward control is an important part of the moving mirror speed control system
Juan Duan, Ying Huang, Qian Guo, Zhanhu Wang, Jianwen Hua   +4 more
core   +1 more source

H4K12 Lactylation Impairs Dopaminergic Neuron Energy Metabolism via the HIF‐1α/NDUFS1 Pathway in Parkinson's Disease

Advanced Science, EarlyView.
Lactate and H4K12la are elevated in Parkinson's disease. Mechanistically, p300‐mediated H4K12la enrichment at the HIF‐1α promoter upregulates HIF‐1α, which represses Ndufs1, impairing mitochondrial respiration and energy homeostasis in DA neurons.
Yingyao Liu, Xi Li, Wenxuan He, Fenglin Li, Qican Ou, Yao Liu, Jiang Lei, Yanzi Bao, Huaxin Huang, Wei Feng, Yuanguang Tang, Xiaohui Qiu, Lu Gan, Yuefei Shen, Jin Wang, Yousheng Xiao   +15 more
wiley   +1 more source

A 24-sectors direct power control-feedforward neural network method of DFIG integrated to dual-rotor wind turbine

, 2021
In this work, a 24-sector direct power control (24-sector DPC) of a doublyfed induction generator (DFIG) based dual-rotor wind turbine (DRWT) is studied.
A 24-sectors direct power control-feedforward neural network method of DFIG integrated to dual-rotor wind turbine
core   +1 more source

Temporally Correlated Stochastic Spiking Neural Networks Enabled by 2D Ferroelectric Synapse‐Neuron Integration

Advanced Science, EarlyView.
We demonstrate a stochastic spiking neural network by integrating an α‐ln2Se3/h‐BN/CIPS FeFET synapse with an Au/CIPS/Au neuron. Unlike conventional memoryless stochastic neurons, the proposed neuron generates stochastic spikes through the evolution of internal ferroelectric polarization states, giving rise to intrinsic temporal correlations and ...
Hanseo Jeon, Ui Do Ji, Sangmin Lee, Gihyeon Bhin, Chang Yong Park, Ji‐Sang Park, Byungchul Jang, Sungjoo Lee   +7 more
wiley   +1 more source

Biofeedback and Feedforward in Telerobotics Control

, 1994
Biofeedback and Feedforward in Telerobotics ...
COSMI, Francesca, A. Rovetta, L. Termite, L. Molinari Tosatti   +3 more
core  

Event-triggered Feedforward Control subject to Actuator Saturation for Disturbance Compensation∗

, 2018
Feedfoward control is widely used to compensate measurable external disturbances. This paper studies feedforward control using an event-triggered sensor. Stability conditions when the feedback control is subject to actuator saturation are derived.
Junfeng Wu, Karl Henrik Johansson, Iwaki, Takuya,, Johansson, Karl Henrik,, Wu, J., Takuya Iwaki   +5 more
core   +1 more source

Data‐Driven and Physics‐Informed Modeling Perspectives for Soft Manipulators: From SINDy to Hybrid Intelligence

Advanced Science, EarlyView.
This Perspective charts the modeling landscape for soft manipulators, spanning sparse system identification, neural dynamics, physics‐informed models and hybrid intelligence. We examine how physics, data, sensing, adaptation and control can assume complementary roles within this landscape, and provide a framework for selecting modeling approaches ...
Wenbo Hui, Yunyi Wang, Teng Zhang, Yang Chen, Min Pan   +4 more
wiley   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source