Distributed control for alpha-heterogeneous dynamically coupled systems

Massioni, Paolo

doi:10.1016/j.sysconle.2014.08.006

Cited by 27 publications

(37 citation statements)

References 19 publications

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“…Model identification remains however computationally challenging to handle the combination of these two matrix structure. Other well-studied structures include interconnected onedimensional strings of subsystems in Rice (2010), or clusters of different subsystems with known connection patterns, named as alpha-heterogeneous in Massioni (2014). However the links between the subsystems in the network might not be known beforehand.…”

Section: Introductionmentioning

confidence: 99%

Kronecker-ARX models in identifying (2D) spatial-temporal systems

Sinquin

Verhaegen

2017

IFAC-PapersOnLine

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In this paper we address the identification of (2D) spatial-temporal dynamical systems governed by the Vector Auto-Regressive (VAR) form. The coefficient-matrices of the VAR model are parametrized as sums of Kronecker products. When the number of terms in the sum is small compared to the size of the matrix, such a Kronecker representation leads to high data compression. Estimating in least-squares sense the coefficient-matrices gives rise to a bilinear estimation problem, which is tackled using a three-stage algorithm. A numerical example demonstrates the advantages of the new modeling paradigm. It leads to comparable performances with the unstructured least-squares estimation of VAR models. However, the number of parameters in the new modeling paradigm grows linearly w.r.t. the number of nodes in the 2D sensor network instead of quadratically in the full unstructured matrix case.

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Section: Introductionmentioning

confidence: 99%

Kronecker-ARX models in identifying (2D) spatial-temporal systems

Sinquin

Verhaegen

2017

IFAC-PapersOnLine

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“…In [4], the concept of was introduced, which can turn topologies that are directed between and undirected within certain groups of subsystems into a virtual symmetric topology. As an alternative, in [7], a Singular Value Decomposition (SVD) is used to handle arbitrary interconnection topologies, while taking up the idea of decomposing the synthesis conditions from [5]. However, in [7], the author fails to realize that the output-feedback controller synthesis problem can actually be posed as an LFT-LPV gain-scheduled controller synthesis problem for which efficient convex optimization can be applied [11].…”

Section: Introductionmentioning

confidence: 99%

“…As an alternative, in [7], a Singular Value Decomposition (SVD) is used to handle arbitrary interconnection topologies, while taking up the idea of decomposing the synthesis conditions from [5]. However, in [7], the author fails to realize that the output-feedback controller synthesis problem can actually be posed as an LFT-LPV gain-scheduled controller synthesis problem for which efficient convex optimization can be applied [11]. In this paper, it is proposed to define a virtual unitarily diagonalizable (normal) interconnection matrix for arbitrary directed topologies.…”

Section: Introductionmentioning

confidence: 99%

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Control of heterogeneous LPV subsystems interconnected through arbitrary and switching directed topologies

Hoffmann

Werner

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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This paper introduces a novel technique for the synthesis of distributed Linear Parameter-Varying (LPV) controllers for heterogeneous LPV systems interconnected through arbitrary, time-varying, directed interaction topologies. A distributed system is constructed from interconnected subsystems for which a novel manipulation of interconnection channels is proposed that renders any interconnection representable as a virtual normal interconnection matrix. This guarantees the existence of a unitary diagonalizing transformation. The synthesis problem can then be essentially solved by Linear Fractional Transformation (LFT)-LPV gain-scheduling synthesis methods, in which the possibly varying eigenvalues of the virtual interconnection matrix act as the scheduling variables of the transformed problem and the computational complexity is in the order of a single subsystem. The method is benchmarked against an existing one and shows superior performance in both a numerical example and the leader-follower formation control problem of LPV quadrocopter models.

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“…This work is based either on the output regulator equations (Huang, 2004;Knobloch, Isidori, & Flockerzi, 1993) or the idea of p-copy (Huang, 2004). Some works have been done on H ∞ control for heterogeneous systems (Dullerud & D'Andrea, 2004;Huang & Ye, 2014;Massioni, 2014;Peymani, Grip, Saberi, Wang, & Fossen, 2014;Scorletti & Duc, 2001).…”

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confidence: 99%

Distributed L2-gain output-feedback control of homogeneous and heterogeneous systems

et al. 2016

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L 2 -gain synchronization LQR based optimal design Output-feedback control a b s t r a c tThe importance of static output feedback (OPFB) design for aircraft control, process control, and elsewhere has been well documented since the 1960s, since full state measurements are not usually available in practical systems. This problem is compounded in the case of multi-agent systems (MAS) where each agent has its own state variable and measured outputs. Therefore, this paper addresses the L 2 -gain OPFB synchronization of linear MAS subject to external disturbances. Both homogeneous and heterogeneous MAS are considered. For homogeneous MAS, it is shown that the L 2 -gain static OPFB synchronization problem of MAS can be cast into the L 2 -gain static OPFB problem for a set of decoupled systems that depend on the graph topology. A modified Riccati equation is introduced which gives the OPFB gain and the coupling gain of the proposed static OPFB control protocol. For heterogeneous MAS, it is shown that the L 2 -gain synchronization problem can be cast into the L 2 -gain static OPFB problem of a set of decoupled systems plus a coupling condition on their dynamic compensators that depends on the graph topology. A certain novel gain matrix is introduced in the dynamics of the control protocol to improve the performance.

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Distributed control for alpha-heterogeneous dynamically coupled systems

Cited by 27 publications

References 19 publications

Kronecker-ARX models in identifying (2D) spatial-temporal systems

Kronecker-ARX models in identifying (2D) spatial-temporal systems

Control of heterogeneous LPV subsystems interconnected through arbitrary and switching directed topologies

Distributed L2-gain output-feedback control of homogeneous and heterogeneous systems

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