SlideShare a Scribd company logo
1 of 17
Download to read offline
faculty of mathematics and
natural sciences
Date 28.03.2012
Controllability of Diffusively-coupled
Multi-agent Systems with Switching
Topologies
Shuo Zhang, M. Kanat Camlibel and Ming Cao
1
faculty of mathematics and
natural sciences
Date 28.03.2012
Outline
• Diffusively Coupled Multi-Agent Systems with Switching
Topologies
• Review: Controllability of Linear Switched Systems
• Graph Partitions
• Main Results
• Concluding Remarks
2
faculty of mathematics and
natural sciences
Date 28.03.2012
Diffusively Coupled Multi-Agent
System with Switching Topologies
• p simple, undirected graphs: G = (V, E), 1  p
• Vertex set: V = {1, 2, . . . , n}
• Edge set of G: E ⊆ V × V
• Leaders of G
• Set of leaders: V
L
= {ℓ
1
, . . . , ℓ
m
} ⊆ V
• Set of followers: V
F
= V  V
L
3
faculty of mathematics and
natural sciences
Date 28.03.2012
• Diffusively coupled multi-agent system with G and V
L
˙j = −
(j,k)∈E
(j − k), if j ∈ V
F
˙j = −
(j,k)∈E
(j − k) + j, if j ∈ V
L
j: agent j’s state; j: external input to agent j
4
faculty of mathematics and
natural sciences
Date 28.03.2012
• Compact form
˙ = −L + M
 = 1 . . . n
T
,  = 1 . . . n
T
, L: the Laplacian
matrix of G, M ∈ Rn×n defined by
[M]jk =
1 if j = ℓ
k
0 otherwise.
• Switched multi-agent system
˙(t) = −Lσ(t)(t) + Mσ(t)(t), (1)
σ(t) : R+ → {1, 2, . . . , p}: a right-continuous piecewise
constant switching signal
5
faculty of mathematics and
natural sciences
Date 28.03.2012
Controllability of Linear Switched
Systems: Review
• A linear switched system
˙(t) = Aσ(t)(t) + Bσ(t)(t), (0) = 0 (2)
 ∈ Rn: the state,  ∈ Rm: the input, σ(t): switching signal
• W0 =
p
=1
im B, Wk+1 =
p
=1
〈A | Wk〉 for all k 0
• ∃ s.t. Wq = W for all q 
• the controllable subspace of (2): W = W
• W is the smallest of the subspaces that are invariant
under A’s for all  ∈ {1, . . . , p} and contain
p
=1
im B
• (2) is controllable if and only if W = Rn
6
faculty of mathematics and
natural sciences
Date 28.03.2012
Graph Partitions
• A cell: any nonempty subset of the vertex set V
• A partition of V: a collection of mutually disjoint cells
π = {C1, . . . , Cr} if
r
j=1
Cj = V
• Characteristic matrix P(π) ∈ Rn×r of π
[P(π)]j =
1 if  ∈ Cj
0 otherwise
1  n, 1 j r.
7
faculty of mathematics and
natural sciences
Date 28.03.2012
• An example of π of V and P(π)
P(π) =


1 0 0
0 1 0
0 0 1
0 1 0
0 0 1


1
2 43 5
• : the set of all the partitions of V
8
faculty of mathematics and
natural sciences
Date 28.03.2012
Partial Order on 
• π1 finer than π2, π1 π2: each cell of π1 is a subset of
some cell of π2
• π1 π2 ⇐⇒ im P(π2) ⊆ im P(π1)
• Example: π1 = {C1, . . . , C5}, C = {}, 1  5
π2 = {{1}, {2, 4}, {3, 5}}
1
2 3 4 5
9
faculty of mathematics and
natural sciences
Date 28.03.2012
: a Complete Lattice
•  with “ ”: a complete lattice
• Any subset  of  has its greatest lower bound ∧
and its least upper bound ∨ in 
• Lemma 1 Given  = {π1, . . . , π} ⊆ , it holds that

=1
im P(π) = im P(

=1
π)
10
faculty of mathematics and
natural sciences
Date 28.03.2012
Almost Equitable Partitions
• An almost equitable partition π = {C1, . . . , Cr} of a given
graph G: for every 1  = j n, ∃ a number bj s.t. any
vertex  ∈ C has bj neighbors in Cj
• Lemma 2 A partition π of G is almost equitable if and
only if im P(π) is L-invariant.
D. M. Cardoso et al. Laplacian eigenvectors and eigenvalues and
almost equitable partitions, European Journal of Combinatorics,
28:665-673, 2007
• AEP(G): the set of all the almost equitable partitions of
G
11
faculty of mathematics and
natural sciences
Date 28.03.2012
• For the collection of graphs {G1, . . . , Gp} and π of G,
define
AEP(π1, . . . , πp) = {π | π ∈ AEP(G) and π π for all }
• Lemma 3 There exists a unique partition, say
π∗ = π∗(π1, . . . , πp) s.t.
π∗
∈ AEP(π1, . . . , πp)
π π∗
for all π ∈ AEP(π1, . . . , πp)
π π for all π ∈ AEP(π1, . . . , πp) =⇒ π∗
π .
12
faculty of mathematics and
natural sciences
Date 28.03.2012
Distance Partitions
• For a connected G = (V, E), the distance partition relative
to a vertex : a partition where each cell is of the form
{ ∈ V | dist(, ) = } for some , 0  dim G
1
2 4 6
3 5
13
faculty of mathematics and
natural sciences
Date 28.03.2012
Main Results
• K: controllable subspace of the switched multi-agent
system ˙(t) = −Lσ(t)(t) + Mσ(t)(t)
• G (1  p): set of leaders V
L
= {ℓ
1
, . . . , ℓ
m
}, set of
followers: V
F
= V  V
L
, a given partition
πL

= {{ℓ
1
}, . . . , {ℓ
m
}, V
F
}
• The collection of G’s:
AEP(πL
1
, . . . , πL
p
) = {π | π ∈ AEP(G) and π πL

for all }
• Theorem 1 K ⊆ im P(π∗(πL
1
, . . . , πL
p
))
• π∗(πL
1
, . . . , πL
p
): the least upper bound of
AEP(πL
1
, . . . , πL
p
)
14
faculty of mathematics and
natural sciences
Date 28.03.2012
• Theorem 2 Suppose that all G’s (1  p) are
connected. Then
dim(K) mx
 ∈ {1, 2, . . . , p}
k ∈ {1, 2, . . . , m}
crd(πD(ℓ
k
; G))
• πD(ℓ
k
; G): the distance partition relative to ℓ
k
in G
• Suppose a given G is connected. Then
dim K mxℓk∈VL crd(πD(ℓk))
– VL: set of leaders
– K: the controllable subspace of the diffusively
coupled multi-agent system associated with G
S. Zhang et al. Controllability of diffusively-coupled multi-agent
systems with general and distance regular coupling topologies.
Proc. of the 50th IEEE CDC, 759-764, 2011
15
faculty of mathematics and
natural sciences
Date 28.03.2012
1 1
2 3 4 5
6 2
3
4
5
6
Figure 1: Upper bound achieved
1
12 3 4 5
6
2
3
4
5
6
Figure 2: Lower bound achieved
16
faculty of mathematics and
natural sciences
Date 28.03.2012
Concluding Remarks
• Tight bounds of the controllable subspace of a diffusively
coupled multi-agent system with switching topologies
• Open questions
• When each agent has more complex dynamics, say
general linear dynamics
• Choice of leaders to guarantee controllability
• Choice of topologies to guarantee controllability...
17

More Related Content

What's hot

On maximal and variational Fourier restriction
On maximal and variational Fourier restrictionOn maximal and variational Fourier restriction
On maximal and variational Fourier restrictionVjekoslavKovac1
 
Optimal L-shaped matrix reordering, aka graph's core-periphery
Optimal L-shaped matrix reordering, aka graph's core-peripheryOptimal L-shaped matrix reordering, aka graph's core-periphery
Optimal L-shaped matrix reordering, aka graph's core-peripheryFrancesco Tudisco
 
Density theorems for Euclidean point configurations
Density theorems for Euclidean point configurationsDensity theorems for Euclidean point configurations
Density theorems for Euclidean point configurationsVjekoslavKovac1
 
Mihai Visinescu "Action-angle variables for geodesic motion on resolved metri...
Mihai Visinescu "Action-angle variables for geodesic motion on resolved metri...Mihai Visinescu "Action-angle variables for geodesic motion on resolved metri...
Mihai Visinescu "Action-angle variables for geodesic motion on resolved metri...SEENET-MTP
 
A Szemeredi-type theorem for subsets of the unit cube
A Szemeredi-type theorem for subsets of the unit cubeA Szemeredi-type theorem for subsets of the unit cube
A Szemeredi-type theorem for subsets of the unit cubeVjekoslavKovac1
 
Numerical Evaluation of Complex Integrals of Analytic Functions
Numerical Evaluation of Complex Integrals of Analytic FunctionsNumerical Evaluation of Complex Integrals of Analytic Functions
Numerical Evaluation of Complex Integrals of Analytic Functionsinventionjournals
 
Density theorems for anisotropic point configurations
Density theorems for anisotropic point configurationsDensity theorems for anisotropic point configurations
Density theorems for anisotropic point configurationsVjekoslavKovac1
 
Core–periphery detection in networks with nonlinear Perron eigenvectors
Core–periphery detection in networks with nonlinear Perron eigenvectorsCore–periphery detection in networks with nonlinear Perron eigenvectors
Core–periphery detection in networks with nonlinear Perron eigenvectorsFrancesco Tudisco
 
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...VjekoslavKovac1
 
Estimates for a class of non-standard bilinear multipliers
Estimates for a class of non-standard bilinear multipliersEstimates for a class of non-standard bilinear multipliers
Estimates for a class of non-standard bilinear multipliersVjekoslavKovac1
 
A sharp nonlinear Hausdorff-Young inequality for small potentials
A sharp nonlinear Hausdorff-Young inequality for small potentialsA sharp nonlinear Hausdorff-Young inequality for small potentials
A sharp nonlinear Hausdorff-Young inequality for small potentialsVjekoslavKovac1
 
Scattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysisScattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysisVjekoslavKovac1
 
Brief summary of signals
Brief summary of signalsBrief summary of signals
Brief summary of signalsaroosa khan
 
Some Examples of Scaling Sets
Some Examples of Scaling SetsSome Examples of Scaling Sets
Some Examples of Scaling SetsVjekoslavKovac1
 
Norm-variation of bilinear averages
Norm-variation of bilinear averagesNorm-variation of bilinear averages
Norm-variation of bilinear averagesVjekoslavKovac1
 
A T(1)-type theorem for entangled multilinear Calderon-Zygmund operators
A T(1)-type theorem for entangled multilinear Calderon-Zygmund operatorsA T(1)-type theorem for entangled multilinear Calderon-Zygmund operators
A T(1)-type theorem for entangled multilinear Calderon-Zygmund operatorsVjekoslavKovac1
 
Lecture 12 orhogonality - 6.1 6.2 6.3
Lecture 12   orhogonality - 6.1 6.2 6.3Lecture 12   orhogonality - 6.1 6.2 6.3
Lecture 12 orhogonality - 6.1 6.2 6.3njit-ronbrown
 
Iterative procedure for uniform continuous mapping.
Iterative procedure for uniform continuous mapping.Iterative procedure for uniform continuous mapping.
Iterative procedure for uniform continuous mapping.Alexander Decker
 
Meta-learning and the ELBO
Meta-learning and the ELBOMeta-learning and the ELBO
Meta-learning and the ELBOYoonho Lee
 

What's hot (20)

On maximal and variational Fourier restriction
On maximal and variational Fourier restrictionOn maximal and variational Fourier restriction
On maximal and variational Fourier restriction
 
Optimal L-shaped matrix reordering, aka graph's core-periphery
Optimal L-shaped matrix reordering, aka graph's core-peripheryOptimal L-shaped matrix reordering, aka graph's core-periphery
Optimal L-shaped matrix reordering, aka graph's core-periphery
 
Density theorems for Euclidean point configurations
Density theorems for Euclidean point configurationsDensity theorems for Euclidean point configurations
Density theorems for Euclidean point configurations
 
Mihai Visinescu "Action-angle variables for geodesic motion on resolved metri...
Mihai Visinescu "Action-angle variables for geodesic motion on resolved metri...Mihai Visinescu "Action-angle variables for geodesic motion on resolved metri...
Mihai Visinescu "Action-angle variables for geodesic motion on resolved metri...
 
A Szemeredi-type theorem for subsets of the unit cube
A Szemeredi-type theorem for subsets of the unit cubeA Szemeredi-type theorem for subsets of the unit cube
A Szemeredi-type theorem for subsets of the unit cube
 
Numerical Evaluation of Complex Integrals of Analytic Functions
Numerical Evaluation of Complex Integrals of Analytic FunctionsNumerical Evaluation of Complex Integrals of Analytic Functions
Numerical Evaluation of Complex Integrals of Analytic Functions
 
Density theorems for anisotropic point configurations
Density theorems for anisotropic point configurationsDensity theorems for anisotropic point configurations
Density theorems for anisotropic point configurations
 
The Gaussian Hardy-Littlewood Maximal Function
The Gaussian Hardy-Littlewood Maximal FunctionThe Gaussian Hardy-Littlewood Maximal Function
The Gaussian Hardy-Littlewood Maximal Function
 
Core–periphery detection in networks with nonlinear Perron eigenvectors
Core–periphery detection in networks with nonlinear Perron eigenvectorsCore–periphery detection in networks with nonlinear Perron eigenvectors
Core–periphery detection in networks with nonlinear Perron eigenvectors
 
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...
 
Estimates for a class of non-standard bilinear multipliers
Estimates for a class of non-standard bilinear multipliersEstimates for a class of non-standard bilinear multipliers
Estimates for a class of non-standard bilinear multipliers
 
A sharp nonlinear Hausdorff-Young inequality for small potentials
A sharp nonlinear Hausdorff-Young inequality for small potentialsA sharp nonlinear Hausdorff-Young inequality for small potentials
A sharp nonlinear Hausdorff-Young inequality for small potentials
 
Scattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysisScattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysis
 
Brief summary of signals
Brief summary of signalsBrief summary of signals
Brief summary of signals
 
Some Examples of Scaling Sets
Some Examples of Scaling SetsSome Examples of Scaling Sets
Some Examples of Scaling Sets
 
Norm-variation of bilinear averages
Norm-variation of bilinear averagesNorm-variation of bilinear averages
Norm-variation of bilinear averages
 
A T(1)-type theorem for entangled multilinear Calderon-Zygmund operators
A T(1)-type theorem for entangled multilinear Calderon-Zygmund operatorsA T(1)-type theorem for entangled multilinear Calderon-Zygmund operators
A T(1)-type theorem for entangled multilinear Calderon-Zygmund operators
 
Lecture 12 orhogonality - 6.1 6.2 6.3
Lecture 12   orhogonality - 6.1 6.2 6.3Lecture 12   orhogonality - 6.1 6.2 6.3
Lecture 12 orhogonality - 6.1 6.2 6.3
 
Iterative procedure for uniform continuous mapping.
Iterative procedure for uniform continuous mapping.Iterative procedure for uniform continuous mapping.
Iterative procedure for uniform continuous mapping.
 
Meta-learning and the ELBO
Meta-learning and the ELBOMeta-learning and the ELBO
Meta-learning and the ELBO
 

Viewers also liked

A Ideologia Inumana E TotalitáRia Do Pndh3
A Ideologia Inumana E TotalitáRia Do Pndh3A Ideologia Inumana E TotalitáRia Do Pndh3
A Ideologia Inumana E TotalitáRia Do Pndh3Seminario de Bioetica
 
Receitas Festa Natalina 08
Receitas Festa Natalina 08Receitas Festa Natalina 08
Receitas Festa Natalina 08frutadiferente
 
20160317褒揚內政部空中勤務總隊故飛行員林振興及本院海岸巡防署海洋巡防總局第十一(特勤)海巡隊故分隊長蔡宗達等2人案
20160317褒揚內政部空中勤務總隊故飛行員林振興及本院海岸巡防署海洋巡防總局第十一(特勤)海巡隊故分隊長蔡宗達等2人案20160317褒揚內政部空中勤務總隊故飛行員林振興及本院海岸巡防署海洋巡防總局第十一(特勤)海巡隊故分隊長蔡宗達等2人案
20160317褒揚內政部空中勤務總隊故飛行員林振興及本院海岸巡防署海洋巡防總局第十一(特勤)海巡隊故分隊長蔡宗達等2人案R.O.C.Executive Yuan
 
Periodico ganador 2013 País de los Estudiantes
Periodico ganador 2013 País de los EstudiantesPeriodico ganador 2013 País de los Estudiantes
Periodico ganador 2013 País de los EstudiantesCiclos Formativos
 
A que velocidade você correria por um sonho ?
A que velocidade você correria por um sonho ?A que velocidade você correria por um sonho ?
A que velocidade você correria por um sonho ?Fios de Histórias
 
Principles of funding
Principles of fundingPrinciples of funding
Principles of fundingaliciacch
 

Viewers also liked (20)

Introduccion a Internet
Introduccion  a InternetIntroduccion  a Internet
Introduccion a Internet
 
Dilema do prisioneiro
Dilema do prisioneiroDilema do prisioneiro
Dilema do prisioneiro
 
A Ideologia Inumana E TotalitáRia Do Pndh3
A Ideologia Inumana E TotalitáRia Do Pndh3A Ideologia Inumana E TotalitáRia Do Pndh3
A Ideologia Inumana E TotalitáRia Do Pndh3
 
Expresso108
Expresso108Expresso108
Expresso108
 
Receitas Festa Natalina 08
Receitas Festa Natalina 08Receitas Festa Natalina 08
Receitas Festa Natalina 08
 
Conceito chapeu slideshare
Conceito chapeu slideshareConceito chapeu slideshare
Conceito chapeu slideshare
 
Calderon de la Barca
Calderon de la BarcaCalderon de la Barca
Calderon de la Barca
 
20160317褒揚內政部空中勤務總隊故飛行員林振興及本院海岸巡防署海洋巡防總局第十一(特勤)海巡隊故分隊長蔡宗達等2人案
20160317褒揚內政部空中勤務總隊故飛行員林振興及本院海岸巡防署海洋巡防總局第十一(特勤)海巡隊故分隊長蔡宗達等2人案20160317褒揚內政部空中勤務總隊故飛行員林振興及本院海岸巡防署海洋巡防總局第十一(特勤)海巡隊故分隊長蔡宗達等2人案
20160317褒揚內政部空中勤務總隊故飛行員林振興及本院海岸巡防署海洋巡防總局第十一(特勤)海巡隊故分隊長蔡宗達等2人案
 
Paseando por Torla (Huesca)
Paseando por Torla (Huesca)Paseando por Torla (Huesca)
Paseando por Torla (Huesca)
 
Cañón del Colorado
Cañón del ColoradoCañón del Colorado
Cañón del Colorado
 
Novas Tecnologias
Novas TecnologiasNovas Tecnologias
Novas Tecnologias
 
Lmv4 flow
Lmv4 flowLmv4 flow
Lmv4 flow
 
Periodico ganador 2013 País de los Estudiantes
Periodico ganador 2013 País de los EstudiantesPeriodico ganador 2013 País de los Estudiantes
Periodico ganador 2013 País de los Estudiantes
 
A que velocidade você correria por um sonho ?
A que velocidade você correria por um sonho ?A que velocidade você correria por um sonho ?
A que velocidade você correria por um sonho ?
 
Monegros
MonegrosMonegros
Monegros
 
Parque Nacional de Kenai (Alaska)
Parque Nacional de Kenai (Alaska)Parque Nacional de Kenai (Alaska)
Parque Nacional de Kenai (Alaska)
 
Sanamed 11(1) 2016 final
Sanamed 11(1) 2016 finalSanamed 11(1) 2016 final
Sanamed 11(1) 2016 final
 
Principles of funding
Principles of fundingPrinciples of funding
Principles of funding
 
Feedback
FeedbackFeedback
Feedback
 
Pitc3
Pitc3Pitc3
Pitc3
 

Similar to Benelux12

Simplified Runtime Analysis of Estimation of Distribution Algorithms
Simplified Runtime Analysis of Estimation of Distribution AlgorithmsSimplified Runtime Analysis of Estimation of Distribution Algorithms
Simplified Runtime Analysis of Estimation of Distribution AlgorithmsPer Kristian Lehre
 
Simplified Runtime Analysis of Estimation of Distribution Algorithms
Simplified Runtime Analysis of Estimation of Distribution AlgorithmsSimplified Runtime Analysis of Estimation of Distribution Algorithms
Simplified Runtime Analysis of Estimation of Distribution AlgorithmsPK Lehre
 
第5回CCMSハンズオン(ソフトウェア講習会): AkaiKKRチュートリアル 1. KKR法
第5回CCMSハンズオン(ソフトウェア講習会): AkaiKKRチュートリアル 1. KKR法第5回CCMSハンズオン(ソフトウェア講習会): AkaiKKRチュートリアル 1. KKR法
第5回CCMSハンズオン(ソフトウェア講習会): AkaiKKRチュートリアル 1. KKR法Computational Materials Science Initiative
 
013_20160328_Topological_Measurement_Of_Protein_Compressibility
013_20160328_Topological_Measurement_Of_Protein_Compressibility013_20160328_Topological_Measurement_Of_Protein_Compressibility
013_20160328_Topological_Measurement_Of_Protein_CompressibilityHa Phuong
 
Trilinear embedding for divergence-form operators
Trilinear embedding for divergence-form operatorsTrilinear embedding for divergence-form operators
Trilinear embedding for divergence-form operatorsVjekoslavKovac1
 
On the Jensen-Shannon symmetrization of distances relying on abstract means
On the Jensen-Shannon symmetrization of distances relying on abstract meansOn the Jensen-Shannon symmetrization of distances relying on abstract means
On the Jensen-Shannon symmetrization of distances relying on abstract meansFrank Nielsen
 
Docslide.us 2002 formulae-and-tables
Docslide.us 2002 formulae-and-tablesDocslide.us 2002 formulae-and-tables
Docslide.us 2002 formulae-and-tablesbarasActuarial
 
On the solvability of a system of forward-backward linear equations with unbo...
On the solvability of a system of forward-backward linear equations with unbo...On the solvability of a system of forward-backward linear equations with unbo...
On the solvability of a system of forward-backward linear equations with unbo...Nikita V. Artamonov
 
Algorithm Design and Complexity - Course 11
Algorithm Design and Complexity - Course 11Algorithm Design and Complexity - Course 11
Algorithm Design and Complexity - Course 11Traian Rebedea
 
Hyers ulam rassias stability of exponential primitive mapping
Hyers  ulam rassias stability of exponential primitive mappingHyers  ulam rassias stability of exponential primitive mapping
Hyers ulam rassias stability of exponential primitive mappingAlexander Decker
 
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...Ceni Babaoglu, PhD
 
A new implementation of k-MLE for mixture modelling of Wishart distributions
A new implementation of k-MLE for mixture modelling of Wishart distributionsA new implementation of k-MLE for mixture modelling of Wishart distributions
A new implementation of k-MLE for mixture modelling of Wishart distributionsFrank Nielsen
 
Random Matrix Theory and Machine Learning - Part 3
Random Matrix Theory and Machine Learning - Part 3Random Matrix Theory and Machine Learning - Part 3
Random Matrix Theory and Machine Learning - Part 3Fabian Pedregosa
 
A Szemerédi-type theorem for subsets of the unit cube
A Szemerédi-type theorem for subsets of the unit cubeA Szemerédi-type theorem for subsets of the unit cube
A Szemerédi-type theorem for subsets of the unit cubeVjekoslavKovac1
 

Similar to Benelux12 (20)

Simplified Runtime Analysis of Estimation of Distribution Algorithms
Simplified Runtime Analysis of Estimation of Distribution AlgorithmsSimplified Runtime Analysis of Estimation of Distribution Algorithms
Simplified Runtime Analysis of Estimation of Distribution Algorithms
 
Simplified Runtime Analysis of Estimation of Distribution Algorithms
Simplified Runtime Analysis of Estimation of Distribution AlgorithmsSimplified Runtime Analysis of Estimation of Distribution Algorithms
Simplified Runtime Analysis of Estimation of Distribution Algorithms
 
第5回CCMSハンズオン(ソフトウェア講習会): AkaiKKRチュートリアル 1. KKR法
第5回CCMSハンズオン(ソフトウェア講習会): AkaiKKRチュートリアル 1. KKR法第5回CCMSハンズオン(ソフトウェア講習会): AkaiKKRチュートリアル 1. KKR法
第5回CCMSハンズオン(ソフトウェア講習会): AkaiKKRチュートリアル 1. KKR法
 
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
 
013_20160328_Topological_Measurement_Of_Protein_Compressibility
013_20160328_Topological_Measurement_Of_Protein_Compressibility013_20160328_Topological_Measurement_Of_Protein_Compressibility
013_20160328_Topological_Measurement_Of_Protein_Compressibility
 
Trilinear embedding for divergence-form operators
Trilinear embedding for divergence-form operatorsTrilinear embedding for divergence-form operators
Trilinear embedding for divergence-form operators
 
g-lecture.pptx
g-lecture.pptxg-lecture.pptx
g-lecture.pptx
 
On the Jensen-Shannon symmetrization of distances relying on abstract means
On the Jensen-Shannon symmetrization of distances relying on abstract meansOn the Jensen-Shannon symmetrization of distances relying on abstract means
On the Jensen-Shannon symmetrization of distances relying on abstract means
 
sada_pres
sada_pressada_pres
sada_pres
 
Docslide.us 2002 formulae-and-tables
Docslide.us 2002 formulae-and-tablesDocslide.us 2002 formulae-and-tables
Docslide.us 2002 formulae-and-tables
 
On the solvability of a system of forward-backward linear equations with unbo...
On the solvability of a system of forward-backward linear equations with unbo...On the solvability of a system of forward-backward linear equations with unbo...
On the solvability of a system of forward-backward linear equations with unbo...
 
Algorithm Design and Complexity - Course 11
Algorithm Design and Complexity - Course 11Algorithm Design and Complexity - Course 11
Algorithm Design and Complexity - Course 11
 
Hyers ulam rassias stability of exponential primitive mapping
Hyers  ulam rassias stability of exponential primitive mappingHyers  ulam rassias stability of exponential primitive mapping
Hyers ulam rassias stability of exponential primitive mapping
 
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...
 
A new implementation of k-MLE for mixture modelling of Wishart distributions
A new implementation of k-MLE for mixture modelling of Wishart distributionsA new implementation of k-MLE for mixture modelling of Wishart distributions
A new implementation of k-MLE for mixture modelling of Wishart distributions
 
file_5.pptx
file_5.pptxfile_5.pptx
file_5.pptx
 
Random Matrix Theory and Machine Learning - Part 3
Random Matrix Theory and Machine Learning - Part 3Random Matrix Theory and Machine Learning - Part 3
Random Matrix Theory and Machine Learning - Part 3
 
2018 MUMS Fall Course - Statistical Representation of Model Input (EDITED) - ...
2018 MUMS Fall Course - Statistical Representation of Model Input (EDITED) - ...2018 MUMS Fall Course - Statistical Representation of Model Input (EDITED) - ...
2018 MUMS Fall Course - Statistical Representation of Model Input (EDITED) - ...
 
cheb_conf_aksenov.pdf
cheb_conf_aksenov.pdfcheb_conf_aksenov.pdf
cheb_conf_aksenov.pdf
 
A Szemerédi-type theorem for subsets of the unit cube
A Szemerédi-type theorem for subsets of the unit cubeA Szemerédi-type theorem for subsets of the unit cube
A Szemerédi-type theorem for subsets of the unit cube
 

Benelux12

  • 1. faculty of mathematics and natural sciences Date 28.03.2012 Controllability of Diffusively-coupled Multi-agent Systems with Switching Topologies Shuo Zhang, M. Kanat Camlibel and Ming Cao 1
  • 2. faculty of mathematics and natural sciences Date 28.03.2012 Outline • Diffusively Coupled Multi-Agent Systems with Switching Topologies • Review: Controllability of Linear Switched Systems • Graph Partitions • Main Results • Concluding Remarks 2
  • 3. faculty of mathematics and natural sciences Date 28.03.2012 Diffusively Coupled Multi-Agent System with Switching Topologies • p simple, undirected graphs: G = (V, E), 1  p • Vertex set: V = {1, 2, . . . , n} • Edge set of G: E ⊆ V × V • Leaders of G • Set of leaders: V L = {ℓ 1 , . . . , ℓ m } ⊆ V • Set of followers: V F = V V L 3
  • 4. faculty of mathematics and natural sciences Date 28.03.2012 • Diffusively coupled multi-agent system with G and V L ˙j = − (j,k)∈E (j − k), if j ∈ V F ˙j = − (j,k)∈E (j − k) + j, if j ∈ V L j: agent j’s state; j: external input to agent j 4
  • 5. faculty of mathematics and natural sciences Date 28.03.2012 • Compact form ˙ = −L + M  = 1 . . . n T ,  = 1 . . . n T , L: the Laplacian matrix of G, M ∈ Rn×n defined by [M]jk = 1 if j = ℓ k 0 otherwise. • Switched multi-agent system ˙(t) = −Lσ(t)(t) + Mσ(t)(t), (1) σ(t) : R+ → {1, 2, . . . , p}: a right-continuous piecewise constant switching signal 5
  • 6. faculty of mathematics and natural sciences Date 28.03.2012 Controllability of Linear Switched Systems: Review • A linear switched system ˙(t) = Aσ(t)(t) + Bσ(t)(t), (0) = 0 (2)  ∈ Rn: the state,  ∈ Rm: the input, σ(t): switching signal • W0 = p =1 im B, Wk+1 = p =1 〈A | Wk〉 for all k 0 • ∃ s.t. Wq = W for all q  • the controllable subspace of (2): W = W • W is the smallest of the subspaces that are invariant under A’s for all  ∈ {1, . . . , p} and contain p =1 im B • (2) is controllable if and only if W = Rn 6
  • 7. faculty of mathematics and natural sciences Date 28.03.2012 Graph Partitions • A cell: any nonempty subset of the vertex set V • A partition of V: a collection of mutually disjoint cells π = {C1, . . . , Cr} if r j=1 Cj = V • Characteristic matrix P(π) ∈ Rn×r of π [P(π)]j = 1 if  ∈ Cj 0 otherwise 1  n, 1 j r. 7
  • 8. faculty of mathematics and natural sciences Date 28.03.2012 • An example of π of V and P(π) P(π) =   1 0 0 0 1 0 0 0 1 0 1 0 0 0 1   1 2 43 5 • : the set of all the partitions of V 8
  • 9. faculty of mathematics and natural sciences Date 28.03.2012 Partial Order on  • π1 finer than π2, π1 π2: each cell of π1 is a subset of some cell of π2 • π1 π2 ⇐⇒ im P(π2) ⊆ im P(π1) • Example: π1 = {C1, . . . , C5}, C = {}, 1  5 π2 = {{1}, {2, 4}, {3, 5}} 1 2 3 4 5 9
  • 10. faculty of mathematics and natural sciences Date 28.03.2012 : a Complete Lattice •  with “ ”: a complete lattice • Any subset  of  has its greatest lower bound ∧ and its least upper bound ∨ in  • Lemma 1 Given  = {π1, . . . , π} ⊆ , it holds that  =1 im P(π) = im P(  =1 π) 10
  • 11. faculty of mathematics and natural sciences Date 28.03.2012 Almost Equitable Partitions • An almost equitable partition π = {C1, . . . , Cr} of a given graph G: for every 1  = j n, ∃ a number bj s.t. any vertex  ∈ C has bj neighbors in Cj • Lemma 2 A partition π of G is almost equitable if and only if im P(π) is L-invariant. D. M. Cardoso et al. Laplacian eigenvectors and eigenvalues and almost equitable partitions, European Journal of Combinatorics, 28:665-673, 2007 • AEP(G): the set of all the almost equitable partitions of G 11
  • 12. faculty of mathematics and natural sciences Date 28.03.2012 • For the collection of graphs {G1, . . . , Gp} and π of G, define AEP(π1, . . . , πp) = {π | π ∈ AEP(G) and π π for all } • Lemma 3 There exists a unique partition, say π∗ = π∗(π1, . . . , πp) s.t. π∗ ∈ AEP(π1, . . . , πp) π π∗ for all π ∈ AEP(π1, . . . , πp) π π for all π ∈ AEP(π1, . . . , πp) =⇒ π∗ π . 12
  • 13. faculty of mathematics and natural sciences Date 28.03.2012 Distance Partitions • For a connected G = (V, E), the distance partition relative to a vertex : a partition where each cell is of the form { ∈ V | dist(, ) = } for some , 0  dim G 1 2 4 6 3 5 13
  • 14. faculty of mathematics and natural sciences Date 28.03.2012 Main Results • K: controllable subspace of the switched multi-agent system ˙(t) = −Lσ(t)(t) + Mσ(t)(t) • G (1  p): set of leaders V L = {ℓ 1 , . . . , ℓ m }, set of followers: V F = V V L , a given partition πL  = {{ℓ 1 }, . . . , {ℓ m }, V F } • The collection of G’s: AEP(πL 1 , . . . , πL p ) = {π | π ∈ AEP(G) and π πL  for all } • Theorem 1 K ⊆ im P(π∗(πL 1 , . . . , πL p )) • π∗(πL 1 , . . . , πL p ): the least upper bound of AEP(πL 1 , . . . , πL p ) 14
  • 15. faculty of mathematics and natural sciences Date 28.03.2012 • Theorem 2 Suppose that all G’s (1  p) are connected. Then dim(K) mx  ∈ {1, 2, . . . , p} k ∈ {1, 2, . . . , m} crd(πD(ℓ k ; G)) • πD(ℓ k ; G): the distance partition relative to ℓ k in G • Suppose a given G is connected. Then dim K mxℓk∈VL crd(πD(ℓk)) – VL: set of leaders – K: the controllable subspace of the diffusively coupled multi-agent system associated with G S. Zhang et al. Controllability of diffusively-coupled multi-agent systems with general and distance regular coupling topologies. Proc. of the 50th IEEE CDC, 759-764, 2011 15
  • 16. faculty of mathematics and natural sciences Date 28.03.2012 1 1 2 3 4 5 6 2 3 4 5 6 Figure 1: Upper bound achieved 1 12 3 4 5 6 2 3 4 5 6 Figure 2: Lower bound achieved 16
  • 17. faculty of mathematics and natural sciences Date 28.03.2012 Concluding Remarks • Tight bounds of the controllable subspace of a diffusively coupled multi-agent system with switching topologies • Open questions • When each agent has more complex dynamics, say general linear dynamics • Choice of leaders to guarantee controllability • Choice of topologies to guarantee controllability... 17