Results 51 to 60 of about 772,875 (164)
Hyper-Data: A Matlab based optimization software for data-driven hyperelasticity
SoftwareXIn this work, a parameter identification software for the data-driven hyperelasticity frameworks proposed by Dal et al. (Journal of the Mechanics and Physics of Solids 179, 105381, 2023), and Tikenoğullarıet al.
Recep Durna +3 more
doaj +1 more source
Direct Data Driven Control Using Noisy Measurements
CoRRSubmitted to IEEE ...
Ramin Esmzad +3 more
openaire +2 more sources
Data‐driven predictive direct load control of refrigeration systems
IET Control Theory & Applications, 2015 A predictive control using subspace identification is applied for the smart grid integration of refrigeration systems under a direct load control scheme. A realistic demand response scenario based on regulation of the electrical power consumption is considered. A receding horizon optimal control is proposed to fulfil two important objectives: to secure
Shafiei, Seyed Ehsan +3 more
openaire +1 more source
Data-Driven Adaptive Control for Laser-Based Additive Manufacturing with Automatic Controller Tuning
Applied Sciences, 2020 Closed-loop control is desirable in direct energy deposition (DED) to stabilize the process and improve the fabrication quality. Most existing DED controllers require system identifications by experiments to obtain plant models or layer-dependent ...
Lequn Chen +5 more
doaj +1 more source
Adversarial destabilization attacks to direct data-driven control
AutomaticaThis study explores the vulnerability of direct data driven control, particularly in the linear quadratic regulator (LQR) problem, to adversarial perturbations in offline collected data. We focus on stealthy attacks that subtly alter training data to destabilize the closed-loop system while evading detection.
openaire +2 more sources
On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches
IEEE Control Systems LettersRecently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-output trajectories without the need to identify a model. In this work, we show that these approaches are equivalent to an indirect approach.
Per Mattsson +3 more
openaire +2 more sources
Bridging prediction and decision: Advances and challenges in data-driven optimization
NexusData-driven approaches have revolutionized traditional optimization methods by integrating prediction with decision-making. This review examines the theoretical foundations, strengths, recent advancements, and limitations of three key methods—sequential ...
Yanzhi Wang +3 more
doaj +1 more source
EnergiesThe transition towards high-renewable power systems introduces high-dimensional nonlinearity and uncertainty, rendering traditional offline look-up table schemes prone to control mismatch against “unseen” contingencies.
Lin Cheng +3 more
doaj +1 more source
Applied SciencesThe problem of power system oscillation stability has become more and more prominent in the context of a high proportion of new energy sources and the gradual increase in power electronic devices.
Hong Fan, Mingze Sun
doaj +1 more source
Direct data-driven filter design for automotive controlled suspensions
2009 European Control Conference (ECC), 2009 This paper investigates the filter design problem for automotive controlled suspensions when no mathematical model of the system is available, but a set of initial experiments can be performed, where also the variable to be estimated is measured.
RUIZ F +2 more
openaire +2 more sources