Results 41 to 50 of about 816,103 (361)

Fuzzy Iterative Learning Contouring Control

open access: yesMathematics
Iterative learning contouring control (ILCC) improves contouring accuracy in multi-axis motion systems via the equivalent contour error formulation. However, its convergence strongly depends on the learning gain.
Thanh-Quan Ta, Shyh-Leh Chen
doaj   +1 more source

Predictive Variable Gain Iterative Learning Control for PMSM

open access: yesJournal of Control Science and Engineering, 2015
A predictive variable gain strategy in iterative learning control (ILC) is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking.
Huimin Xu, Xuedong Zhang, Xiangjie Liu
doaj   +1 more source

Online Iterative Learning Contouring Control

open access: yesIEEE Access
An online iterative learning contouring control (online ILCC) method is proposed to improve the contouring performance of multi-axis motion systems by simultaneously reducing position and orientation contour errors.
Thanh-Quan Ta, Shyh-Leh Chen
doaj   +1 more source

Iterative Learning Control Based on Nesterov Accelerated Gradient Method

open access: yesIEEE Access, 2019
Based on Nesterov accelerated gradient method, the problem of iterative learning control for a class of linear discrete-time systems is considered in this paper.
Panpan Gu, Senping Tian, YangQuan Chen
doaj   +1 more source

Research on Positioning Control Strategy for a Hydraulic Support Pushing System Based on Iterative Learning

open access: yesActuators, 2023
At present, the positioning control of the hydraulic support pushing systems in fully mechanized mining faces uses an electrohydraulic directional valve as the control component, while the current research mainly focuses on servo valves, proportional ...
Tengyan Hou   +5 more
doaj   +1 more source

Epigenetic heterogeneity and plasticity in therapy‐induced tumor states through single‐cell multi‐omics

open access: yesMolecular Oncology, EarlyView.
Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim   +3 more
wiley   +1 more source

Iterative Learning Control with Forgetting Factor for Linear Distributed Parameter Systems with Uncertainty

open access: yesJournal of Control Science and Engineering, 2014
Iterative learning control is an intelligent control algorithm which imitates human learning process. Based on this concept, this paper discussed iterative learning control problem for a class parabolic linear distributed parameter systems with ...
Xisheng Dai   +3 more
doaj   +1 more source

Robust stability for iterative learning control [PDF]

open access: yes2009 American Control Conference, 2009
The phenomenon of long term instability of iterative learning control systems is examined. The concept of stability with respect to a specified trajectory is used to define a nonlinear biased gap metric. The resulting robust stability theorem, applied in a 2D setting, is used to prove the robust stability of a set of ILC algorithms engaged in ...
Richard Bradley, Mark French
openaire   +1 more source

Rapid screening of staphylokinase protein variants using an unpurified cell‐free expression system

open access: yesFEBS Open Bio, EarlyView.
An unpurified cell‐free protein synthesis (CFPS) platform enables rapid functional screening of staphylokinase variants. Direct plasminogen‐activation assays performed in microplate format provide real‐time activity readouts, allowing rapid identification and ranking of variants with improved or reduced fibrinolytic activity without protein ...
Maria Tomková   +3 more
wiley   +1 more source

Experimentally supported 2D systems based iterative learning control law design for error convergence and performance

open access: yes, 2010
This paper considers iterative learning control law design for both trial-to-trial error convergence and along the trial performance. It is shown how a class of control laws can be designed using the theory of linear repetitive processes for this problem
Hladowski, Lukasz   +5 more
core   +1 more source

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