Approximate solutions for large-scale piecewise deterministic control systems arising in manufacturing flow control models

1994 ◽  
Vol 10 (2) ◽  
pp. 142-152 ◽  
Author(s):  
C. van Delft
Keyword(s):  
Flow Control ◽  
Control Systems ◽  
Large Scale ◽  
Approximate Solutions ◽  
Control Models ◽  
Deterministic Control

scholarly journals Hydraulic optimisation of multiple flow control locations for the design of local real time control systems

2020 ◽  
pp. 1-10
Author(s):  
M. Eulogi ◽  
S. Ostojin ◽  
P. Skipworth ◽  
J. D. Shucksmith ◽  
A. Schellart
Keyword(s):  
Flow Control ◽  
Control Systems ◽  
Real Time ◽  
Real Time Control ◽  
Time Control

scholarly journals Resource Allocation in Large-Scale Wireless Control Systems with Graph Neural Networks

2020 ◽  
Vol 53 (2) ◽  
pp. 2634-2641
Author(s):  
Vinicius Lima ◽  
Mark Eisen ◽  
Konstatinos Gatsis ◽  
Alejandro Ribeiro
Keyword(s):  
Neural Networks ◽  
Resource Allocation ◽  
Control Systems ◽  
Large Scale ◽  
Wireless Control ◽  
Graph Neural Networks

Role-Based Information Flow Control Models

2014 ◽  
Author(s):  
Shigenari Nakamura ◽  
Dilewaer Doulikun ◽  
Ailixier Aikebaier ◽  
Tomoya Enokido ◽  
Makoto Takizawa
Keyword(s):  
Flow Control ◽  
Information Flow ◽  
Information Flow Control ◽  
Control Models ◽  
Role Based

An Approach to the Simulation of Large-scale Interconnected MIMO Control Systems

1987 ◽  
Vol 20 (12) ◽  
pp. 395-400
Author(s):  
G.M. Dimirovski ◽  
M.M. Carevski ◽  
D.J. Mamucevski
Keyword(s):  
Control Systems ◽  
Large Scale ◽  
Mimo Control

scholarly journals Algebraic multigrid methods

Acta Numerica ◽  
2017 ◽  
Vol 26 ◽  
pp. 591-721 ◽  
Author(s):  
Jinchao Xu ◽  
Ludmil Zikatanov
Keyword(s):  
Minimization Problem ◽  
Large Scale ◽  
Multigrid Method ◽  
Approximate Solutions ◽  
Multigrid Methods ◽  
Algebraic Multigrid ◽  
Unified Framework ◽  
Smoothed Aggregation ◽  
Algebraic Multigrid Methods ◽  
Coarse Space

This paper provides an overview of AMG methods for solving large-scale systems of equations, such as those from discretizations of partial differential equations. AMG is often understood as the acronym of ‘algebraic multigrid’, but it can also be understood as ‘abstract multigrid’. Indeed, we demonstrate in this paper how and why an algebraic multigrid method can be better understood at a more abstract level. In the literature, there are many different algebraic multigrid methods that have been developed from different perspectives. In this paper we try to develop a unified framework and theory that can be used to derive and analyse different algebraic multigrid methods in a coherent manner. Given a smoother$R$for a matrix$A$, such as Gauss–Seidel or Jacobi, we prove that the optimal coarse space of dimension$n_{c}$is the span of the eigenvectors corresponding to the first$n_{c}$eigenvectors$\bar{R}A$(with$\bar{R}=R+R^{T}-R^{T}AR$). We also prove that this optimal coarse space can be obtained via a constrained trace-minimization problem for a matrix associated with$\bar{R}A$, and demonstrate that coarse spaces of most existing AMG methods can be viewed as approximate solutions of this trace-minimization problem. Furthermore, we provide a general approach to the construction of quasi-optimal coarse spaces, and we prove that under appropriate assumptions the resulting two-level AMG method for the underlying linear system converges uniformly with respect to the size of the problem, the coefficient variation and the anisotropy. Our theory applies to most existing multigrid methods, including the standard geometric multigrid method, classical AMG, energy-minimization AMG, unsmoothed and smoothed aggregation AMG and spectral AMGe.

Data Flow Control Systems: an Example of Safety Validation

1993 ◽  
pp. 9-20 ◽  
Author(s):  
Cinzia Bernardeschi ◽  
Luca Simoncini ◽  
Andrea Bondavalli
Keyword(s):  
Flow Control ◽  
Control Systems ◽  
Data Flow ◽  
Data Flow Control
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