Results 141 to 150 of about 263,923 (197)

Uncertainty aware hybrid learning framework for fast and safe charging of lithium-ion batteries using multi-fidelity observers. [PDF]

Sci Rep
Parimala CHH, Varma PS, Kumar RS, Singh AR, Mohapatra B, Addis HK.   +5 more
europepmc   +1 more source

A sparrow search algorithm-optimized LSTM framework with EMD denoising for rolling element bearing remaining useful life prediction. [PDF]

Sci Rep
Li Q, Zhang B, Fang X.
europepmc   +1 more source

A hierarchical lateral stability control strategy of distributed drive electric vehicles based on extended Kalman filter and integral terminal sliding mode control. [PDF]

PLoS One
Wang J, Zhao Y, Gao W, Deng Z.
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Advances in pharmacokinetic-pharmacodynamic modeling for anesthesia, 1987-2024: a review. [PDF]

Front Pharmacol
Tulbah Y, Aljamaan I.
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Comparative Accuracy of Machine Learning and GBLUP for Predicting Genomic Estimated Breeding Values in Chickens. [PDF]

Genes (Basel)
Chai H, Yang Y, Wang D, Ning C, Zhang X, Wang W, Zhang Q, Bao H, Tang H.   +8 more
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GIS-based neural network framework for zoonotic cutaneous leishmaniasis risk mapping in Western Iran. [PDF]

Environ Monit Assess
Parto Dezfooli F, Valadan Zoej MJ, Youssefi F, Alatab S, Ghaderpour E.   +4 more
europepmc   +1 more source

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Nonlinear Model Predictive Control

2011
Nonlinear model predictive control (NMPC) is widely used in the process and chemical industries and increasingly for applications, such as those in the automotive industry, which use higher data sampling rates. Nonlinear Model Predictive Control is a thorough and rigorous introduction to NMPC for discrete-time and sampled-data systems.
Grüne, Lars, Pannek, Jürgen
openaire   +3 more sources

NONLINEAR MODEL PREDICTIVE CONTROL

Chemical Engineering Communications, 1990
Abstract Nonlinear Model Predictive Control (NMPC), a strategy for constrained, feedback control of nonlinear processes, has been developed. The algorithm uses a simultaneous solution and optimization approach to determine the open-loop optimal manipulated variable trajectory at each sampling instant.
ASHUTOSH A. PATWARDHAN, JAMES B. RAWLINGS, THOMAS F. EDGAR   +2 more
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