Zobrazeno 1 - 10
of 973
pro vyhledávání: '"Paul, M. J."'
Autor:
Hof, Paul M. J. Van den, Shi, Shengling, Fonken, Stefanie J. M., Ramaswamy, Karthik R., Hjalmarsson, Håkan, Dankers, Arne G.
When estimating models of of a multivariable dynamic system, a typical condition for consistency is to require the input signals to be persistently exciting, which is guaranteed if the input spectrum is positive definite for a sufficient number of fr
Externí odkaz:
http://arxiv.org/abs/2409.03883
Control in a classical transfer function or state-space setting typically views a controller as a signal processor: sensor outputs are mapped to actuator inputs. In behavioral system theory, control is simply viewed as interconnection; the interconne
Externí odkaz:
http://arxiv.org/abs/2210.06268
In order to identify a system (module) embedded in a dynamic network, one has to formulate a multiple-input estimation problem that necessitates certain nodes to be measured and included as predictor inputs. However, some of these nodes may not be me
Externí odkaz:
http://arxiv.org/abs/2208.10995
Modeling and control of dynamical systems rely on measured data, which contains information about the system. Finite data measurements typically lead to a set of system models that are unfalsified, i.e., that explain the data. The problem of data-inf
Externí odkaz:
http://arxiv.org/abs/2202.09266
This paper deals with dynamic networks in which the causality relations between the vertex signals are represented by linear time-invariant transfer functions (modules). Considering an acyclic network where only a subset of its vertices are measured
Externí odkaz:
http://arxiv.org/abs/2201.07548
In this paper we develop new data informativity based controller synthesis methods that extend existing frameworks in two relevant directions: a more general noise characterization in terms of cross-covariance bounds and informativity conditions for
Externí odkaz:
http://arxiv.org/abs/2111.14193
Publikováno v:
Volume 141, July 2022, 110295
Identification methods for dynamic networks typically require prior knowledge of the network and disturbance topology, and often rely on solving poorly scalable non-convex optimization problems. While methods for estimating network topology are avail
Externí odkaz:
http://arxiv.org/abs/2106.07548
Physical dynamic networks most commonly consist of interconnections of physical components that can be described by diffusive couplings. These diffusive couplings imply that the cause-effect relationships in the interconnections are symmetric and the
Externí odkaz:
http://arxiv.org/abs/2106.01813
In classical approaches of dynamic network identification, in order to identify a system (module) embedded in a dynamic network, one has to formulate a Multi-input-Single-output (MISO) identification problem that requires identification of a parametr
Externí odkaz:
http://arxiv.org/abs/2105.10901
This paper considers dynamic networks where vertices and edges represent manifest signals and causal dependencies among the signals, respectively. We address the problem of how to determine if the dynamics of a network can be identified when only par
Externí odkaz:
http://arxiv.org/abs/2105.03187