Results 91 to 100 of about 816,103 (361)
Iterative Learning Control Applied to a Gantry Robot and Conveyor System
, 2010 Synchronisation is routinely required to coordinate the actions of the various sub-systems involved in process applications. This is commonly achieved through direct mechanical coupling, involving gears, drive belts and cams.
Ratcliffe, James +3 more
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Advanced Engineering Materials, EarlyView.The present study investigates recycling of NiTi shape memory alloys via vacuum induction melting. An ingot was synthesized from elemental Ni and Ti and subjected to three subsequent remelting cycles. Remelting increases process durations and impurity levels and adversely affects microstructures and functional properties.
Sakia Sophia Noorzayee +7 more
wiley +1 more source
Optimization of the Production of Rubber Compounds Using Mathematical Models
Advanced Engineering Materials, EarlyView.Rubber compounds were mixed in a batch internal mixer, and symbolic regression was used to derive mathematical models linking recipe and process parameters to ram path, torque, and mixing quality (incorporation, dispersion, distribution). Subsequent optimization with evolutionary algorithms identified operating conditions that reduce specific energy ...
Anke Bardehle +7 more
wiley +1 more source
An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials
Advanced Engineering Materials, EarlyView.An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut +16 more
wiley +1 more source
Multivariable norm optimal iterative learning control with auxiliary optimization
, 2013 The paper describes a substantial extension of Norm Optimal Iterative Learning Control (NOILC) that permits tracking of a class of finite dimensional reference signals whilst simultaneously converging to the solution of a constrained quadratic ...
Owens, D.H., Freeman, C.T., Chu, B.
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Optimal control of non-stationary differential linear repetitive processes
, 2008 Differential repetitive processes are a distinct class of continuous discrete 2D linear systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such systems is the fact that information ...
Rogers, E +7 more
core +1 more source
Advanced Engineering Materials, EarlyView.This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source
Comments on 'On the equivalence of causal LTI iterative learning control and feedback control
, 2004 The area of Iterative Learning Control (ILC) now has a large and increasing body of research with an increasing number of applications (supported by a sizable number of actual experimental verification studies). The paper by Goldsmith (Automatica, 38, pp.
Owens, D H, Rogers, E
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Frequency domain iterative feedforward/feedback tuning for MIMO ANVC
, 2010 A new gradient estimation method is proposed that relies on efficient computation of the negative gradient of the average linear quadratic cost function completely in the frequency domain.
Veres, Sandor M. +3 more
core +1 more source
Suppressing intersample behavior in iterative learning control [PDF]
, 2008 Iterative Learning Control (ILC) is a control strategy to improve the performance of digital batch repetitive processes. Due to its digital implementation, discrete time ILC approaches do not guarantee good intersample behavior.
Wijdeven, van de, J.J.M. +8 more
core +1 more source