Scaling the Size of a Multiagent Formation via Distributed Feedback
|
|
- Meredith Dean
- 6 years ago
- Views:
Transcription
1 Scaling the Size of a Multiagent Formation via Distributed Feedback Samuel Coogan, Murat Arcak, Magnus Egerstedt Abstract We consider a multiagent coordination problem where the objective is to steer a group of agents into a formation that translates along a predefined trajectory velocity. Unlike previous control strategies that require a static desired formation or set of desired formations, we introduce a strategy in which one agent assigns a scale for the formation the remaining agents adjust to the new scale. Thus, the formation can dynamically adapt to changes in the environment in group objectives or respond to perceived threats. We introduce two strategies: one that requires agents to communicate estimates of the desired formation scale along edges of a communication network one that only requires relative position sensing among agents. We show that the former strategy guarantees stability for any desired connected formation. For the latter strategy, we present a geometric constraint which can be used with the small gain theorem. I. INTRODUCTION Group coordination of mobile agents using distributed feedback laws has been an active area of control theory research in recent years. The main emphasis of such coordination problems is to achieve a desired group behavior using only local feedback rules [], [], [], []. In particular, formation control as a desired group behavior has received considerable attention, such as in [5], [6], [7]. Use of artificial potential fields for formation control is explored in [8], formation control strategies with communication topology switching are investigated in [9], strategies allowing the desired formation to switch among various configurations is presented in []. In this paper, we consider the problem of formation control along a velocity trajectory when only one agent knows the desired formation size. The remaining follower agents estimate the desired formation size proceed with a cooperative control law utilizing this estimate. By allowing the size of the formation to change, the group can dynamically adapt to changes in the environment such as unforeseen obstacles along the trajectory path, adapt to changes in group objectives, or respond to threats. We present two strategies for updating the desired formation size estimates. In the first strategy, we assume a communication topology exists among the agents, allowing the agents to share formation size estimates. Rather than permitting the leader to simply dictate the desired formation scale, which requires a communication structure heavily dependent on the identity of the leader, we present an update rule utilizing the internal estimates of multiple neighbors Samuel Coogan Murat Arcak are with the Department of Electrical Engineering Computer Sciences, University of California, Berkeley {scoogan,arcak}@eecs.berkeley.edu Magnus Egerstedt is with the School of Electrical Computer Engineering, Georgia Institute of Technology magnus@ece.gatech.edu while still guaranteeing stability for any desired formation. The advantage to this approach is that the follower agents do not need to know which agent is the leader. For example, the leader could be changed during operation without communicating this change to the remaining agents. In some scenarios, we are interested in the minimum amount of information required to allow formation scaling control as it may be undesirable or impossible for the agents to actively communicate with one another. In the second strategy, we assume that only relative position information defined by a sensing topology is available to each agent. In this strategy, no interagent communication is needed. This paper is organized as follows: In Section II, we introduce the problem statement. In Section III we introduce a cooperative control strategy that requires interagent communication guarantees stability for any connected formation. In Section IV we introduce a strategy that does not require interagent communication. In this case, we use the small gain theorem to derive a sufficient geometric condition for stability. In Section V, we present simulation results, in Section VI, we summarize our results provide directions for future research. II. PROBLEM DEFINITION Consider a team of n agents each represented by the vector x i R p, i =,..., n, assume each agent is modeled with double integrator dynamics: ẍ i = f i. () We define x = [x T x T x T... x T n ] T v = ẋ to denote the stacked vector containing the velocities of all of the agents. We assume there is a position sensing topology by which agents are capable of inferring the relative position of other agents. We represent this topology with a sensing graph with n nodes representing the n agents. We assume relative position sensing is bidirectional, if agent i agent j have access to the quantity x i x j, then the ith jth nodes of the sensing graph are connected by an edge. Suppose there are m edges in the sensing graph. We arbitrarily assign a direction to each edge of the sensing graph define the n m incidence matrix D as: + if node i is the head of edge j d ij = if node i is the tail of edge j otherwise where d ij is the ijth entry of D. Since the graph is bidirectional, this choice of direction does not affect any of the following results.
2 Suppose the agents are to maintain a certain formation by maintaining the relative interagent distances defined by the sensing topology. Suppose, in addition, it is allowable for the formation size to change, one agent (for notational simplicity without loss of generality, agent n) chooses a constant target formation scale λ. Each other agent possesses a local formation scalar λ i representing its estimate of λ. We define λ := [λ λ... λ n ] T. Our goal is to achieve the following three desired group behaviors: B) Each agent reaches in the limit a common velocity vector v(t) for the group, i.e. lim ẋ i v(t) =, i =,..., n. () t B) Each agent s formation scaling factor reaches λ in the limit, i.e. lim λ i λ =, i =,..., n. () t B) The relative position of two agents connected by an edge k n z k := d ik x i () i= converges to a prescribed target value λ zk d for each k =,..., m. Note that z d = [z d T... z dt ] T must be designed such that z d R(D T I p ) where R denotes range space, is the Kronecker product operator, I p denotes the p p identity matrix. III. FORMATION SCALING WITH COMMUNICATION BETWEEN AGENTS We begin by defining a linear controller for the input f i to each agent: m f i = d ij (z j λ i zj d ) k i (ẋ i v(t)) + v(t) (5) j= where k i > is a damping coefficient. Let n denote the length n column vector of all ones define Γ := ṽ(t) := ẋ(t) n v(t) (6) m j= d jzj d... m j= d njz d j (7) which can be written compactly as Γ := diag{(d I p )z d } (I n p ) where the diag{ } operation converts a vector into a diagonal matrix with the elements of the vector along the diagonal. Then (5) can be written in matrix form as f = (D I p )z + Γλ (K I p )ṽ + n v(t) (8) where K := diag{ [ ] k... k n } f = [ ] f T... fn T T. Given that λ n = λ is constant, we now seek to design an update rule for λ,..., λ n. Suppose agents are capable of communicating a scalar quantity to other agents. One obvious approach allows the leader agent to communicate the desired formation scale to its neighbors, who in turn communicate the desired scale to their neighbors, etc. This approach requires each agent to know a priori which neighboring agent forms a path to the leader, may not be robust to dropped communication links, does not adapt well to a reassignment of the leader since a different communication tree is required for each possible leader agent. We now present an alternative approach in which a communication structure update rule are designed to address these shortcomings. Suppose each agent can share its formation scaling factor with neighboring agents as defined by a communication graph with n nodes labeled ν,..., ν n where an edge points from agent i to agent j if i has access to j s scaling factor, this edge is denoted by (ν i, ν j ). We will assume there exists a directed path from any node ν i to the leader for all i n (in the undirected case, this condition is equivalent to connectedness). We denote the communication graph by G = (V, E) where V = {ν,..., ν n } is the vertex set of the graph E V V is the edge set of the graph. Let Γ i = (D i I p )z d be the p block of Γ corresponding to agent i s control input where D i indicates the ith row of the incidence matrix D, define tunable parameters α ij for i, j =,..., n such that α ij > iff (ν i, ν j ) E. We propose the following control strategy: λ i = α ij (λ i λ j ) βp i Γ T i (v i v(t)) (9) j N c i for i =... n where β is a tunable, scalar parameter such that β, N c i = {j : (ν i, ν j ) E}, p i is a positive scalar designed below to ensure stability. Note that λn =. This control strategy is intuitively appealing because it includes an agreement expression among the scaling parameters an additional term dependent on the velocity error. Define z := z λ z d () λ := (λ λ n ). () Let λ λ be λ λ with the last element removed, i.e. λ = [λ λ... λ n ] T () λ = (λ λ n ). () Then objectives B B amount to asymptotic stability of the origin for the closed loop system with state X = [ṽ T z T λt ] T. () Let L be an n n matrix defined by { n k=,k i l ij = α ik, i = j α ij, i j. (5)
3 Since agent n does not update its scaling factor via feedback, we set the last row of L to. L is called the graph Laplacian of the communication graph, when we refer to the graph Laplacian of a given graph with edge weights, we imply the above construction. Define L to be L with the last row removed, define L to be L with the last row the last column removed. Also, let Γ be equal to Γ with the last column removed, let P = diag{ [ p... p n ] }. Then (9) can be written in matrix form as λ = λ = Lλ βp Γ T ṽ (6) = L λ + Lλ n βp Γ T ṽ (7) = L λ βp Γ T ṽ, (8) where we use the fact that L n = n hence L n = n where n denotes the length n column vector of all zeros. It is apparent from (8) that f = (D I p )z + Γλ (K I p )ṽ + n v(t) (9) This gives = (D I p ) z λ (D I p )z d + Γ λ + λ Γ n (K I p )ṽ + n v(t). () ṽ = (D I p ) z + Γ λ (K I p )ṽ. () Because λ n =, () can be written as ṽ = (D I p ) z + Γ λ (K I p )ṽ. () Let n m denote the n m matrix all zeros. When the dimensions are clear, the subscript is omitted. The dynamics of (8) (8) can then be formulated as a homogenous linear system K I p D I p Γ Ẋ = D T I p X () βp Γ T L evolving on the following invariant subspace of R (n+m)p+n : S X = {(ṽ, z, λ) : ṽ R np, z R(D T I p ), λ R n }. () To facilitate the proof of the stability of (), we now introduce a generalization of Lemma.6 in [] to the case of a directed communication topology. Lemma. All eigenvalues of L are in the open right half plane. Proof: Let B(a, R) be the closed disk of radius R centered at a. The Gershgorin circle theorem states that all eigenvalues of L are located in the union of the following n disks: ( B i l ii, ) n j=,j i l ij, i =,..., n. (5) But l ii n j=,j i l ij, so the union of the n disks lie in the closed right half plane may only intersect the jωaxis at the origin. It remains to show that L does not have a zero eigenvalue. As a proof by contradiction, assume L has a nontrivial nullspace such that Lw = for some nontrivial w. Because the last row of L is T n, we have [ w T ] T N (L) where N denotes null space. By our assumption that the communication graph contains a directed spanning tree, the rank of L is n []. Therefore, {} forms a basis for the nullspace of L, forming a contradiction. We require the p i s in (9) to be chosen such that L T P + P L > (6) where P is a diagonal matrix with the p i s along the diagonal. A diagonal solution P > to (6) exists because L has nonnegative diagonal nonpositive off-diagonal entries eigenvalues in the open right half plane ([], Chapter 6, Theorem.). In many practical cases, p i can be taken to be for all i. In particular, if the subgraph induced by removing the leader agent its edges is balanced (i.e., n j=,j i α ji = n j=,j i α ij), then L T +L is positive definite P can be taken to be the identity as shown in the following corollary. This encompasses any case in which the communication graph is bidirectional. Corollary. Let G l be the subgraph of the communication graph induced by removing the leader agent. If G l is balanced, then L T + L >. Proof: Let E be the set of edges connected to node n in G with the same edge weighting but with the head tail of these edges reversed. Define G := (V, E E), i.e. G is the communication graph with additional edges pointing towards the leader node. By construction, G is balanced. Let L be the graph Laplacian corresponding to G. Note that L is also equal to L with the last row column removed. Because G is balanced, L s = L + (L ) T []. Note that L s = L + L T is a principle subminor of L s, hence L s. Also note that, because G is balanced, T is a left eigenvector of L s. As a proof by contradiction, suppose L s has a nontrivial nullspace such that Lw = for some nontrivial w. Because T L s =, we see that the last row of L s is a linear combination of the remaining rows, hence y = [ w T ] T N (L s ). But {} forms a basis for the nullspace of L s, a contradiction. Theorem. The origin of () () with P selected as in (6) is asymptotically stable. Proof: Case. (β > ) We construct a Lyapunov function V = (ṽt ṽ + z T z + β λt P λ). (7) Let Q := L T P + P L. Then V = ṽ T (K I p )ṽ λ T ( β Q ) λ. (8)
4 5 (a) Fig.. (a) -agent, 5-edge sensing graph with edge indexing based on the monitoring subgraph. (b) One possible monitoring graph constructed from the sensing graph. We now use LaSalle s invariance principle. Note that V means ṽ, λ. From (), this implies z N (D I p ) for all t. Recalling that z R(D T I p ) = N (D I p ), we see that z. Hence, V iff z, λ, z. Therefore, X = is asymptotically stable. Case. (β = ) The resulting transition matrix of () is then upper triangular, therefore the system is asymptotically stable iff the subsystems [ ] ṽ z = (b) [ K Ip D I p D T I p ] [ṽ ] z (9) λ = L λ () are asymptotically stable. () is clearly asymptotically stable, (9) can be seen to be asymptotically stable by a Lyapunov argument again invoking LaSalle s invariance principle as in Case. IV. FORMATION SCALING WITH NO COMMUNICATION We again consider agent dynamics of the form in (5). However, we now present an update rule for λ i that does not require agents to share local scaling estimates with other agents, i.e. only relative position information is shared along edges in the sensing graph. From the sensing graph, we construct a directed spanning tree (also known as an arborescence []) rooted at the leader with the following properties: ) The arborescence contains all the vertices a (directed) subset of the edges of the sensing graph. ) Each vertex except the leader is the head of exactly one edge. The leader agent is the head of no edge. ) A directed path exists from the leader node to all other nodes. We call this graph the monitoring graph. Without loss of generality for notational purposes, assume the leader is again agent n. To simplify notation analysis, for each edge in the monitoring graph, we number the corresponding edge in the sensing graph to match the head node index of that edge in the monitoring graph, we arbitrarily index the remaining edges of the sensing graph. Fig. shows an example of a formation with the sensing graph, one possible construction of the monitoring graph, the induced edge indexing scheme. The proposed update rule for other agents is ( ) λ i = λ i (zd i )T z i zi d for i =,..., n. () This means that each agent monitors one edge to update its local formation scaling factor. For notational simplicity, we let (z d )T z d T =... () (z d n )T z d n Ψ = diag{(d I p )z d } (I n p ), i.e. Ψ is equal to Γ with the last row column removed. We can then write the dynamics of (8) () as a homogenous linear system ṽ K I p D I p Γ ṽ z = D T I p z () λ ˆT In λ where ˆT = [T (n ) p(m n+) ]. Objectives B B amount to asymptotic stability of the origin of (). We can represent this cooperative control system as the interconnection of two subsystems as in Fig. : ṽ, z subsystem: [ ] ṽ = z [ K Ip D I p D T I p y = [ p(n ) pn λ subsystem: ] [ṽ ] + z I p(n ) p p(n ) pm p(n ) u () ] [ ] ṽ I p(n ) p(n ) p(m n+). z (5) λ = I n λ + T y (6) u = Ψ λ. (7) Note that both subsystems are stable. The stability of the composite system can be checked using a number of techniques, including the small gain theorem. Despite its potential conservatism, the small gain theorem gives a stability condition that can be verified based on the geometry of the formation. Because both subsystems are linear stable, asymptotic stability of the origin of () follows from the small gain theorem stability condition. We first present a geometric interpretation of the L gain of the λ subsystem: Theorem. The L gain of the λ subsystem is: m j= d ijz d j γ = max i=,...,n z d i. (8) Proof: Let H(s) be the transfer function of the λ subsystem, i.e. H(s) = s+ ΨT. To calculate γ = sup ω H(jω), we note that the supremum occurs when
5 I + u = Ψ λ z [ ] I [ ] K D D T I p Ψ λ s+ [ṽ ] T y = z. z n Fig.. Interconnection of subsystems () (5) (6) (7). Dimension subscripts are removed for clarity. ω = is equal to ΨT. Observe that Ψ T are block diagonal. Therefore, it is clear that ΨT consists of n blocks of dimension p p along the diagonal. The singular values of ΨT are simply the singular values of each p p block. Let g i = m j= d ijzj d for i =,..., n. We have that γ is the largest singular value of ΨT, the p p blocks of (z ΨT take the form g d i )T d (z i. The singular values of g zi d i )T i { } zi d g are i zi d,. This leads to a geometric criterion ensuring stability when the small gain theorem is applied: if γ is the L gain of the ṽ, z subsystem, then γ < γ ensures stability of the composite system []. Note that γ depends on the damping coefficients on the incidence matrix D, but does not depend on the specifics of the formation geometry. We now present a bound on γ in the case when the damping coefficients are identical for all agents: Theorem. Assume the agents have uniform velocity damping (i.e. k := k =... = k n ). Let µ,..., µ n be the positive eigenvalues of the unweighted sensing graph Laplacian DD T. Let / µ i if k µ i ρ i = µ i if k < (9) µ i k µ i k for i =,..., n. The L gain of the ṽ, z subsystem, denoted by γ, satisfies γ max{ρ i }. () i Proof: Define the following: [ ] [ ] A A A := K Ip D I = p A A D T () I p pm pm [ ] I B := n I p () (m+) (n ) C := [ (n ) n I n (n ) (m n+) ] Ip. () We have that the transfer matrix from u to y of the ṽ, z subsystem is G(s) = C ( si (n+m)p A ) B. () If we view (si (n+m)p A) as a block matrix, we see from the structure of B C that G(s) is a submatrix of the lower left block of (si (n+m)p A). We can compute the lower left block using block matrix inversion techniques obtain an equivalent expression for G(s): G(s) = [ [ ] ] Ip(n ) I p(n ) p(n ) p(m n+) F (s) p p(n ) where (5) F (s) = ((si mp A ) + A (si np A ) ) (6) ( A )(si np A ) = [ ((s + ks)i m + D T D) D T ] I p. (7) We are interested in the value of F (s) evaluated along the jω-axis. First suppose that s = jω where ω. Let D = UΣ V T be a singular value decomposition of D with U R n n, V R m m orthonormal, Σ R n m diagonal. Note that the nonzero diagonal elements of Σ are µ i for i =,..., n. It follows that (s + ks)i m + D T D = V ((s + ks)i + Σ T Σ )V T, therefore we can write ((s + ks)i m + D T D) = V Σ V T (8) where Σ is a diagonal matrix with the values /(s + ks + µ i ), i =,..., n along the diagonal. Thus we see that for s = jω, ω, F (s) = (V Σ Σ T U T ) I p. (9) Since V U are orthonormal, the nonzero singular values of F (s) are the nonzero singular values of Σ Σ T, which are µi / s +ks+µ i, i =,..., n. In the case when s =, we have [ ((s + ks)i m + D T D) D T ] I p = D + I p (5) lim s where + denotes the Moore-Penrose pseudoinverse, the singular values of D + are / µ i [5]. Thus the p(n ) nonzero singular values of F (s) when s = jω, ω R are the second order transfer functions µi σ i (s) = s, i =,..., n, (5) + ks + µ i each occurring with multiplicity p due to the Kronecker product with the identity in (7). We have thatsup ω σ i (jω) = ρ i. We also have that G(s) is a submatrix of F (s). It follows that G(s) F (s) for all s, hence γ max i {ρ i }. Corollary. Assume the agents have uniform velocity damping k. Let µ denote the smallest positive eigenvalue of DD T, known as the Fiedler eigenvalue of the graph Laplacian. For sufficiently large k, we have γ µ. (5) Proof: Let µ i be the nonzero eigenvalues of DD T. If k µ i for all i, then ρ i = µi for all i, the result follows.
6 λi z j z j d Evolution of formation scaling factors over time without interagent communication 6 8 Evolution of edge lengths over time without interagent communication 6 8 Fig.. Simulation results for a -agent, 5-edge formation with no interagent communication, rom initial positions a desired formation scaling factor of. λi z j z j d Evolution of formation scaling factors over time with interagent communication 6 8 Evolution of edge lengths over time with interagent communication 6 8 Fig.. Simulation results for a -agent, 5-edge formation with interagent communication, rom initial positions a desired formation scaling factor of. V. SIMULATION RESULTS Consider the formation in the plane depicted in Fig. (a) where each edge is assigned an arbitrary direction, D corresponds to the chosen node edge numbering direction assignment, agent is the leader, the monitoring subgraph is shown in Fig. (b). We let z d = [ ] T.9, (5) which is consistent with the shape depicted in Fig. (a). We have that µ = is the smallest positive eigenvalue of DD T µ = is the largest eigenvalue of DD T. Suppose k := k = k = k = k. If k 8, then, by Corollary, γ. (5) Using the geometric condition presented in Theorem we have that γ =.. Thus γ γ =. =.9 < (55) therefore the system with no interagent communication is stable. Fig. shows simulation results in the case of no interagent communication with λ =, λ i () = for i =,..., n, the agents initial positions initialized romly. Fig. shows simulation results for the same formation desired formation size, but interagent communication is allowed the communication graph is equivalent to the unweighted sensing graph with edges pointing towards the leader agent removed. The plots demonstrate that the control strategy with communication converges to the desired formation more quickly, yet the improvement is modest. VI. CONCLUSIONS In this paper, we introduce increased flexibility adaptivity to the stard formation maintenance problem by presenting cooperative control strategies that allow a multiagent team to dynamically alter its formation size. We have presented a strategy that relies on interagent communication produces a stable cooperative control system given any desired connected formation, we have presented a strategy that relies only on relative position information with stability results that are dependent on the desired formation velocity damping. In the latter case, we have introduced a simple geometric criterion which may be used with the small gain theorem to determine stability. In both cases, these strategies can be combined with other objectives such as collision avoidance using stard algorithms. REFERENCES [] W. Ren, R. W. Beard, E. M. Atkins, A survey of consensus problems in multi-agent coordination, in Proceedings of the American Control Conference, vol., pp , 5. [] R. Olfati-Saber, J. A. Fax, R. M. Murray, Consensus cooperation in networked multi-agent systems, Proceedings of the IEEE, vol. 95, no., pp. 5, 7. [] R. Olfati-Saber R. Murray, Consensus problems in networks of agents with switching topology time-delays, Automatic Control, IEEE Transactions on, vol. 9, pp. 5 5, Sept.. [] A. Jadbabaie, J. Lin, A. S. Morse, Coordination of groups of mobile autonomous agents using nearest neighbor rules, Automatic Control, IEEE Transactions on, vol. 8, no. 6, pp. 988,. [5] H. Tanner, A. Jadbabaie, G. Pappas, Stable flocking of mobile agents, part i: fixed topology, in Decision Control,. Proceedings. nd IEEE Conference on, vol., pp. 5, Dec.. [6] J. Lawton, R. Beard, B. Young, A decentralized approach to formation maneuvers, Robotics Automation, IEEE Transactions on, vol. 9, pp. 9 9, Dec.. [7] J. Desai, J. Ostrowski, V. Kumar, Controlling formations of multiple mobile robots, in Robotics Automation, 998. Proceedings. 998 IEEE International Conference on, vol., pp , May 998. [8] P. Ogren, E. Fiorelli, N. E. Leonard, Cooperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment, Automatic Control, IEEE Transactions on, vol. 9, no. 8, pp. 9,. [9] J. Fax R. Murray, Information flow cooperative control of vehicle formations, Automatic Control, IEEE Transactions on, vol. 9, pp , Sept.. [] H. Axelsson, A. Muhammad, M. Egerstedt, Autonomous formation switching for multiple, mobile robots, in IFAC Conference on Analysis Design of Hybrid Systems,. [] M. Mesbahi M. Egerstedt, Graph Theoretic Methods in Multiagent Networks. Princeton University Press,. [] A. Berman R. J. Plemmons, Nonnegative Matrices in the Mathematical Sciences. Society for Industrial Applied Mathematics, 99. [] C. Berge, The Theory of Graphs. Dover Publications, Inc.,. [] H. K. Khalil, Nonlinear Systems. Prentice Hall, third ed.,. [5] A. J. Laub, Matrix Analysis for Scientists Engineers. Society for Industrial Applied Mathematics, 5.
Scaling the Size of a Formation using Relative Position Feedback
Scaling the Size of a Formation using Relative Position Feedback Samuel Coogan a, Murat Arcak a a Department of Electrical Engineering and Computer Sciences, University of California, Berkeley Abstract
More informationA Graph-Theoretic Characterization of Controllability for Multi-agent Systems
A Graph-Theoretic Characterization of Controllability for Multi-agent Systems Meng Ji and Magnus Egerstedt Abstract In this paper we continue our pursuit of conditions that render a multi-agent networked
More informationOn the Controllability of Nearest Neighbor Interconnections
On the Controllability of Nearest Neighbor Interconnections Herbert G. Tanner Mechanical Engineering Department University of New Mexico Albuquerque, NM 87 Abstract In this paper we derive necessary and
More informationComplex Laplacians and Applications in Multi-Agent Systems
1 Complex Laplacians and Applications in Multi-Agent Systems Jiu-Gang Dong, and Li Qiu, Fellow, IEEE arxiv:1406.186v [math.oc] 14 Apr 015 Abstract Complex-valued Laplacians have been shown to be powerful
More informationFlocking while Preserving Network Connectivity
Flocking while Preserving Network Connectivity Michael M Zavlanos, Ali Jadbabaie and George J Pappas Abstract Coordinated motion of multiple agents raises fundamental and novel problems in control theory
More informationConsensus of Information Under Dynamically Changing Interaction Topologies
Consensus of Information Under Dynamically Changing Interaction Topologies Wei Ren and Randal W. Beard Abstract This paper considers the problem of information consensus among multiple agents in the presence
More informationGraph Theoretic Methods in the Stability of Vehicle Formations
Graph Theoretic Methods in the Stability of Vehicle Formations G. Lafferriere, J. Caughman, A. Williams gerardol@pd.edu, caughman@pd.edu, ancaw@pd.edu Abstract This paper investigates the stabilization
More informationConsensus Seeking in Multi-agent Systems Under Dynamically Changing Interaction Topologies
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, SUBMITTED FOR PUBLICATION AS A TECHNICAL NOTE. 1 Consensus Seeking in Multi-agent Systems Under Dynamically Changing Interaction Topologies Wei Ren, Student Member,
More informationFormation Control of Nonholonomic Mobile Robots
Proceedings of the 6 American Control Conference Minneapolis, Minnesota, USA, June -6, 6 FrC Formation Control of Nonholonomic Mobile Robots WJ Dong, Yi Guo, and JA Farrell Abstract In this paper, formation
More informationDecentralized Stabilization of Heterogeneous Linear Multi-Agent Systems
1 Decentralized Stabilization of Heterogeneous Linear Multi-Agent Systems Mauro Franceschelli, Andrea Gasparri, Alessandro Giua, and Giovanni Ulivi Abstract In this paper the formation stabilization problem
More informationConsensus Protocols for Networks of Dynamic Agents
Consensus Protocols for Networks of Dynamic Agents Reza Olfati Saber Richard M. Murray Control and Dynamical Systems California Institute of Technology Pasadena, CA 91125 e-mail: {olfati,murray}@cds.caltech.edu
More informationDistributed Coordinated Tracking With Reduced Interaction via a Variable Structure Approach Yongcan Cao, Member, IEEE, and Wei Ren, Member, IEEE
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 57, NO. 1, JANUARY 2012 33 Distributed Coordinated Tracking With Reduced Interaction via a Variable Structure Approach Yongcan Cao, Member, IEEE, and Wei Ren,
More informationConsensus Problem in Multi-Agent Systems with Communication Channel Constraint on Signal Amplitude
SICE Journal of Control, Measurement, and System Integration, Vol 6, No 1, pp 007 013, January 2013 Consensus Problem in Multi-Agent Systems with Communication Channel Constraint on Signal Amplitude MingHui
More informationRobust Connectivity Analysis for Multi-Agent Systems
Robust Connectivity Analysis for Multi-Agent Systems Dimitris Boskos and Dimos V. Dimarogonas Abstract In this paper we provide a decentralized robust control approach, which guarantees that connectivity
More informationConsensus Seeking in Multi-agent Systems Under Dynamically Changing Interaction Topologies
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, SUBMITTED FOR PUBLICATION AS A TECHNICAL NOTE. Consensus Seeking in Multi-agent Systems Under Dynamically Changing Interaction Topologies Wei Ren, Student Member,
More informationTracking control for multi-agent consensus with an active leader and variable topology
Automatica 42 (2006) 1177 1182 wwwelseviercom/locate/automatica Brief paper Tracking control for multi-agent consensus with an active leader and variable topology Yiguang Hong a,, Jiangping Hu a, Linxin
More informationConsensus Tracking for Multi-Agent Systems with Nonlinear Dynamics under Fixed Communication Topologies
Proceedings of the World Congress on Engineering and Computer Science Vol I WCECS, October 9-,, San Francisco, USA Consensus Tracking for Multi-Agent Systems with Nonlinear Dynamics under Fixed Communication
More informationTheory and Applications of Matrix-Weighted Consensus
TECHNICAL REPORT 1 Theory and Applications of Matrix-Weighted Consensus Minh Hoang Trinh and Hyo-Sung Ahn arxiv:1703.00129v3 [math.oc] 6 Jan 2018 Abstract This paper proposes the matrix-weighted consensus
More informationFormation Stabilization of Multiple Agents Using Decentralized Navigation Functions
Formation Stabilization of Multiple Agents Using Decentralized Navigation Functions Herbert G. Tanner and Amit Kumar Mechanical Engineering Department University of New Mexico Albuquerque, NM 873- Abstract
More informationDecentralized Control of Vehicle Formations
Portland State University PDXScholar Mathematics and Statistics Faculty Publications and Presentations Fariborz Maseeh Department of Mathematics and Statistics Decentralized Control of Vehicle Formations
More informationConsensus Based Formation Control Strategies for Multi-vehicle Systems
Proceedings of the 6 American Control Conference Minneapolis, Minnesota, USA, June 14-16, 6 FrA1.5 Consensus Based Formation Control Strategies for Multi-vehicle Systems Wei Ren Abstract In this paper
More informationCoordination of Groups of Mobile Autonomous Agents Using Nearest Neighbor Rules
University of Pennsylvania ScholarlyCommons Departmental Papers (ESE) Department of Electrical & Systems Engineering June 2003 Coordination of Groups of Mobile Autonomous Agents Using Nearest Neighbor
More informationFast Linear Iterations for Distributed Averaging 1
Fast Linear Iterations for Distributed Averaging 1 Lin Xiao Stephen Boyd Information Systems Laboratory, Stanford University Stanford, CA 943-91 lxiao@stanford.edu, boyd@stanford.edu Abstract We consider
More informationAverage-Consensus of Multi-Agent Systems with Direct Topology Based on Event-Triggered Control
Outline Background Preliminaries Consensus Numerical simulations Conclusions Average-Consensus of Multi-Agent Systems with Direct Topology Based on Event-Triggered Control Email: lzhx@nankai.edu.cn, chenzq@nankai.edu.cn
More informationMULTI-AGENT TRACKING OF A HIGH-DIMENSIONAL ACTIVE LEADER WITH SWITCHING TOPOLOGY
Jrl Syst Sci & Complexity (2009) 22: 722 731 MULTI-AGENT TRACKING OF A HIGH-DIMENSIONAL ACTIVE LEADER WITH SWITCHING TOPOLOGY Yiguang HONG Xiaoli WANG Received: 11 May 2009 / Revised: 16 June 2009 c 2009
More informationStability Analysis of Stochastically Varying Formations of Dynamic Agents
Stability Analysis of Stochastically Varying Formations of Dynamic Agents Vijay Gupta, Babak Hassibi and Richard M. Murray Division of Engineering and Applied Science California Institute of Technology
More informationConsensus Algorithms are Input-to-State Stable
05 American Control Conference June 8-10, 05. Portland, OR, USA WeC16.3 Consensus Algorithms are Input-to-State Stable Derek B. Kingston Wei Ren Randal W. Beard Department of Electrical and Computer Engineering
More information1520 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 49, NO. 9, SEPTEMBER Reza Olfati-Saber, Member, IEEE, and Richard M. Murray, Member, IEEE
1520 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 49, NO. 9, SEPTEMBER 2004 Consensus Problems in Networks of Agents With Switching Topology and Time-Delays Reza Olfati-Saber, Member, IEEE, and Richard
More informationAgreement Problems in Networks with Directed Graphs and Switching Topology
Technical Report CIT-CDS 3 5 Agreement Problems in Networks with Directed Graphs and Switching Topology Reza Olfati Saber Richard M. Murray Control and Dynamical Systems California Institute of Technology
More informationDiscrete Double Integrator Consensus
Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, Dec. 9-11, 28 Discrete Double Integrator Consensus David W. Casbeer, Randy Beard, and A. Lee Swindlehurst Abstract A distributed
More informationStructural Consensus Controllability of Singular Multi-agent Linear Dynamic Systems
Structural Consensus Controllability of Singular Multi-agent Linear Dynamic Systems M. ISAL GARCÍA-PLANAS Universitat Politècnica de Catalunya Departament de Matèmatiques Minería 1, sc. C, 1-3, 08038 arcelona
More informationFORMATIONS OF FORMATIONS: HIERARCHY AND STABILITY
FORMATIONS OF FORMATIONS: HIERARCHY AND STABILITY Anca Williams, Sonja lavaški, Tariq Samad ancaw@pdxedu, sonjaglavaski@honeywellcom Abstract In this paper we will consider a hierarchy of vehicle formations
More informationObservability and Controllability Verification in Multi-Agent Systems through Decentralized Laplacian Spectrum Estimation
1 Observability and Controllability Verification in Multi-Agent Systems through Decentralized Laplacian Spectrum Estimation Mauro Franceschelli, Simone Martini, Magnus Egerstedt, Antonio Bicchi, Alessandro
More informationConsensus Stabilizability and Exact Consensus Controllability of Multi-agent Linear Systems
Consensus Stabilizability and Exact Consensus Controllability of Multi-agent Linear Systems M. ISABEL GARCÍA-PLANAS Universitat Politècnica de Catalunya Departament de Matèmatiques Minería 1, Esc. C, 1-3,
More informationA Graph-Theoretic Characterization of Structural Controllability for Multi-Agent System with Switching Topology
Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference Shanghai, P.R. China, December 16-18, 29 FrAIn2.3 A Graph-Theoretic Characterization of Structural Controllability
More informationDecentralized Formation Control and Connectivity Maintenance of Multi-Agent Systems using Navigation Functions
Decentralized Formation Control and Connectivity Maintenance of Multi-Agent Systems using Navigation Functions Alireza Ghaffarkhah, Yasamin Mostofi and Chaouki T. Abdallah Abstract In this paper we consider
More informationExact Consensus Controllability of Multi-agent Linear Systems
Exact Consensus Controllability of Multi-agent Linear Systems M. ISAEL GARCÍA-PLANAS Universitat Politècnica de Catalunya Departament de Matèmatiques Minería 1, Esc. C, 1-3, 08038 arcelona SPAIN maria.isabel.garcia@upc.edu
More informationObservability and Controllability Verification in Multi-Agent Systems through Decentralized Laplacian Spectrum Estimation
Observability and Controllability Verification in Multi-Agent Systems through Decentralized Laplacian Spectrum Estimation Mauro Franceschelli, Simone Martini, Magnus Egerstedt, Antonio Bicchi, Alessandro
More informationCombining distance-based formation shape control with formation translation
Combining distance-based formation shape control with formation translation Brian D O Anderson, Zhiyun Lin and Mohammad Deghat Abstract Steepest descent control laws can be used for formation shape control
More informationSliding mode control for coordination in multi agent systems with directed communication graphs
Proceedings of the European Control Conference 27 Kos, Greece, July 2-5, 27 TuC8.2 Sliding mode control for coordination in multi agent systems with directed communication graphs Antonella Ferrara, Giancarlo
More informationConsensus of Hybrid Multi-agent Systems
Consensus of Hybrid Multi-agent Systems Yuanshi Zheng, Jingying Ma, and Long Wang arxiv:52.0389v [cs.sy] 0 Dec 205 Abstract In this paper, we consider the consensus problem of hybrid multi-agent system.
More informationZeno-free, distributed event-triggered communication and control for multi-agent average consensus
Zeno-free, distributed event-triggered communication and control for multi-agent average consensus Cameron Nowzari Jorge Cortés Abstract This paper studies a distributed event-triggered communication and
More informationConsensus Problems on Small World Graphs: A Structural Study
Consensus Problems on Small World Graphs: A Structural Study Pedram Hovareshti and John S. Baras 1 Department of Electrical and Computer Engineering and the Institute for Systems Research, University of
More informationStabilizing a Multi-Agent System to an Equilateral Polygon Formation
Stabilizing a Multi-Agent System to an Equilateral Polygon Formation Stephen L. Smith, Mireille E. Broucke, and Bruce A. Francis Abstract The problem of stabilizing a group of agents in the plane to a
More informationNon-Collision Conditions in Multi-agent Robots Formation using Local Potential Functions
2008 IEEE International Conference on Robotics and Automation Pasadena, CA, USA, May 19-23, 2008 Non-Collision Conditions in Multi-agent Robots Formation using Local Potential Functions E G Hernández-Martínez
More informationNotes on averaging over acyclic digraphs and discrete coverage control
Notes on averaging over acyclic digraphs and discrete coverage control Chunkai Gao Francesco Bullo Jorge Cortés Ali Jadbabaie Abstract In this paper, we show the relationship between two algorithms and
More informationSJÄLVSTÄNDIGA ARBETEN I MATEMATIK
SJÄLVSTÄNDIGA ARBETEN I MATEMATIK MATEMATISKA INSTITUTIONEN, STOCKHOLMS UNIVERSITET Consensus problems for multi-agent systems av Hongmei Zhao 2010 - No 2 MATEMATISKA INSTITUTIONEN, STOCKHOLMS UNIVERSITET,
More informationOn the Stability of Distance-based Formation Control
Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, Dec. 9-11, 2008 On the Stability of Distance-based Formation Control Dimos V. Dimarogonas and Karl H. Johansson Abstract
More informationActive Passive Networked Multiagent Systems
Active Passive Networked Multiagent Systems Tansel Yucelen and John Daniel Peterson Abstract This paper introduces an active passive networked multiagent system framework, which consists of agents subject
More informationStable Flocking Motion of Mobile Agents Following a Leader in Fixed and Switching Networks
International Journal of Automation and Computing 1 (2006) 8-16 Stable Flocking Motion of Mobile Agents Following a Leader in Fixed and Switching Networks Hui Yu, Yong-Ji Wang Department of control science
More informationStability and Disturbance Propagation in Autonomous Vehicle Formations : A Graph Laplacian Approach
Stability and Disturbance Propagation in Autonomous Vehicle Formations : A Graph Laplacian Approach Francesco Borrelli*, Kingsley Fregene, Datta Godbole, Gary Balas* *Department of Aerospace Engineering
More informationUsing Orientation Agreement to Achieve Planar Rigid Formation
28 American Control Conference Westin Seattle Hotel, Seattle, Washington, USA June 11-13, 28 WeBI2.8 Using Orientation Agreement to Achieve Planar Rigid Formation He Bai Murat Arca John T. Wen Abstract
More informationarxiv: v1 [cs.ro] 8 Aug 2016
Enforcing Biconnectivity in Multi-robot Systems Mehran Zareh, Lorenzo Sabattini, and Cristian Secchi arxiv:1608.02286v1 [cs.ro] 8 Aug 2016 Abstract Connectivity maintenance is an essential task in multi-robot
More informationDiscrete-time Consensus Filters on Directed Switching Graphs
214 11th IEEE International Conference on Control & Automation (ICCA) June 18-2, 214. Taichung, Taiwan Discrete-time Consensus Filters on Directed Switching Graphs Shuai Li and Yi Guo Abstract We consider
More informationNCS Lecture 8 A Primer on Graph Theory. Cooperative Control Applications
NCS Lecture 8 A Primer on Graph Theory Richard M. Murray Control and Dynamical Systems California Institute of Technology Goals Introduce some motivating cooperative control problems Describe basic concepts
More informationMax-Consensus in a Max-Plus Algebraic Setting: The Case of Fixed Communication Topologies
Max-Consensus in a Max-Plus Algebraic Setting: The Case of Fixed Communication Topologies Behrang Monajemi Nejad, Sid Ahmed Attia and Jörg Raisch Control Systems Group ( Fachgebiet Regelungssysteme ),
More informationConsensus Problems in Complex-Weighted Networks
Consensus Problems in Complex-Weighted Networks arxiv:1406.1862v1 [math.oc] 7 Jun 2014 Jiu-Gang Dong, Li Qiu Abstract Consensus problems for multi-agent systems in the literature have been studied under
More informationMulti-Robotic Systems
CHAPTER 9 Multi-Robotic Systems The topic of multi-robotic systems is quite popular now. It is believed that such systems can have the following benefits: Improved performance ( winning by numbers ) Distributed
More informationFinite-Time Distributed Consensus in Graphs with Time-Invariant Topologies
Finite-Time Distributed Consensus in Graphs with Time-Invariant Topologies Shreyas Sundaram and Christoforos N Hadjicostis Abstract We present a method for achieving consensus in distributed systems in
More informationDistributed Adaptive Consensus Protocol with Decaying Gains on Directed Graphs
Distributed Adaptive Consensus Protocol with Decaying Gains on Directed Graphs Štefan Knotek, Kristian Hengster-Movric and Michael Šebek Department of Control Engineering, Czech Technical University, Prague,
More informationDistributed Robust Consensus of Heterogeneous Uncertain Multi-agent Systems
Distributed Robust Consensus of Heterogeneous Uncertain Multi-agent Systems Zhongkui Li, Zhisheng Duan, Frank L. Lewis. State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics
More informationSE(N) Invariance in Networked Systems
SE(N) Invariance in Networked Systems Cristian-Ioan Vasile 1 and Mac Schwager 2 and Calin Belta 3 Abstract In this paper, we study the translational and rotational (SE(N)) invariance properties of locally
More informationRECENTLY, the study of cooperative control of multiagent
24 IEEE/CAA JOURNAL OF AUTOMATICA SINICA, VOL., NO. 2, APRIL 24 Consensus Robust Output Regulation of Discrete-time Linear Multi-agent Systems Hongjing Liang Huaguang Zhang Zhanshan Wang Junyi Wang Abstract
More informationMulti-agent Second Order Average Consensus with Prescribed Transient Behavior
Multi-agent Second Order Average Consensus with Prescribed Transient Behavior Luca Macellari, Yiannis Karayiannidis and Dimos V. Dimarogonas Abstract The problem of consensus reaching with prescribed transient
More informationControllability analysis of multi-agent systems using relaxed equitable partitions
1 Int J Systems, Control and Communications, Vol 2, Nos 1/2/3, 21 Controllability analysis of multi-agent systems using relaxed equitable partitions Simone Martini* Interdepartmental Research Center, E
More informationObtaining Consensus of Multi-agent Linear Dynamic Systems
Obtaining Consensus of Multi-agent Linear Dynamic Systems M I GRCÍ-PLNS Universitat Politècnica de Catalunya Departament de Matemàtica plicada Mineria 1, 08038 arcelona SPIN mariaisabelgarcia@upcedu bstract:
More informationAutomatica. Distributed discrete-time coordinated tracking with a time-varying reference state and limited communication
Automatica 45 (2009 1299 1305 Contents lists available at ScienceDirect Automatica journal homepage: www.elsevier.com/locate/automatica Brief paper Distributed discrete-time coordinated tracking with a
More informationA Control Lyapunov Function Approach to Multiagent Coordination
IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 18, NO. 5, OCTOBER 2002 847 A Control Lyapunov Function Approach to Multiagent Coordination Petter Ögren, Magnus Egerstedt, Member, IEEE, and Xiaoming
More informationAlmost Global Asymptotic Formation Stabilization Using Navigation Functions
University of New Mexico UNM Digital Repository Mechanical Engineering Faculty Publications Engineering Publications 10-8-2004 Almost Global Asymptotic Formation Stabilization Using Navigation Functions
More informationConsensus seeking on moving neighborhood model of random sector graphs
Consensus seeking on moving neighborhood model of random sector graphs Mitra Ganguly School of Physical Sciences, Jawaharlal Nehru University, New Delhi, India Email: gangulyma@rediffmail.com Timothy Eller
More informationDecentralized Consensus Based Control Methodology for Vehicle Formations in Air and Deep Space
American Control Conference Marriott Waterfront, Baltimore, MD, USA June 3-July, hb5.5 Decentralized Consensus Based Control Methodology for Vehicle Formations in Air and Deep Space Miloš S. Stanković,
More informationA Note to Robustness Analysis of the Hybrid Consensus Protocols
American Control Conference on O'Farrell Street, San Francisco, CA, USA June 9 - July, A Note to Robustness Analysis of the Hybrid Consensus Protocols Haopeng Zhang, Sean R Mullen, and Qing Hui Abstract
More informationNecessary and Sufficient Graphical Conditions for Formation Control of Unicycles
Necessary and Sufficient Graphical Conditions for Formation Control of Unicycles Zhiyun Lin, Bruce Francis, and Manfredi Maggiore Abstract The feasibility problem is studied of achieving a specified formation
More informationDistributed Tracking ControlforLinearMultiagent Systems With a Leader of Bounded Unknown Input
518 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 58, NO. 2, FEBRUARY 2013 Distributed Tracking ControlforLinearMultiagent Systems With a Leader of Bounded Unknown Input Zhongkui Li, Member,IEEE, Xiangdong
More informationApproximate Manipulability of Leader-Follower Networks
Approximate Manipulability of Leader-Follower Networks Hiroaki Kawashima and Magnus Egerstedt Abstract We introduce the notion of manipulability to leader-follower networks as a tool to analyze how effective
More informationOn Bifurcations. in Nonlinear Consensus Networks
On Bifurcations in Nonlinear Consensus Networks Vaibhav Srivastava Jeff Moehlis Francesco Bullo Abstract The theory of consensus dynamics is widely employed to study various linear behaviors in networked
More informationH CONTROL OF NETWORKED MULTI-AGENT SYSTEMS
Jrl Syst Sci & Complexity (2009) 22: 35 48 H CONTROL OF NETWORKED MULTI-AGENT SYSTEMS Zhongkui LI Zhisheng DUAN Lin HUANG Received: 17 April 2008 c 2009 Springer Science Business Media, LLC Abstract This
More informationTechnical Report. A survey of multi-agent formation control: Position-, displacement-, and distance-based approaches
Technical Report A survey of multi-agent formation control: Position-, displacement-, and distance-based approaches Number: GIST DCASL TR 2012-02 Kwang-Kyo Oh, Myoung-Chul Park, and Hyo-Sung Ahn Distributed
More informationTowards Decentralization of Multi-robot Navigation Functions
Towards Decentralization of Multi-robot Navigation Functions Herbert G Tanner and Amit Kumar Mechanical Engineering Dept University of New Mexico Abstract We present a navigation function through which
More informationReaching a Consensus in a Dynamically Changing Environment A Graphical Approach
Reaching a Consensus in a Dynamically Changing Environment A Graphical Approach M. Cao Yale Univesity A. S. Morse Yale University B. D. O. Anderson Australia National University and National ICT Australia
More informationTotally distributed motion control of sphere world multi-agent systems using Decentralized Navigation Functions
Totally distributed motion control of sphere world multi-agent systems using Decentralized Navigation Functions Dimos V. Dimarogonas, Kostas J. Kyriakopoulos and Dimitris Theodorakatos Abstract A distributed
More informationFormation Control and Network Localization via Distributed Global Orientation Estimation in 3-D
Formation Control and Network Localization via Distributed Global Orientation Estimation in 3-D Byung-Hun Lee and Hyo-Sung Ahn arxiv:1783591v1 [cssy] 1 Aug 17 Abstract In this paper, we propose a novel
More informationLinear Algebra and its Applications
Linear Algebra and its Applications 431 (9) 71 715 Contents lists available at ScienceDirect Linear Algebra and its Applications journal homepage: www.elsevier.com/locate/laa On the general consensus protocol
More informationGraph Weight Design for Laplacian Eigenvalue Constraints with Multi-Agent Systems Applications
211 5th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC) Orlando, FL, USA, December 12-15, 211 Graph Weight Design for Laplacian Eigenvalue Constraints with Multi-Agent
More informationVirtual leader approach to coordinated control of multiple mobile agents with asymmetric interactions
Physica D 213 (2006) 51 65 www.elsevier.com/locate/physd Virtual leader approach to coordinated control of multiple mobile agents with asymmetric interactions Hong Shi, Long Wang, Tianguang Chu Intelligent
More informationOutput Synchronization on Strongly Connected Graphs
Output Synchronization on Strongly Connected Graphs Nikhil Chopra and Mark W. Spong Abstract In this paper we study output synchronization of networked multiagent systems. The agents, modeled as nonlinear
More informationOn Distributed Coordination of Mobile Agents with Changing Nearest Neighbors
On Distributed Coordination of Mobile Agents with Changing Nearest Neighbors Ali Jadbabaie Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia, PA 19104 jadbabai@seas.upenn.edu
More informationCooperative Control and Mobile Sensor Networks
Cooperative Control and Mobile Sensor Networks Cooperative Control, Part I, A-C Naomi Ehrich Leonard Mechanical and Aerospace Engineering Princeton University and Electrical Systems and Automation University
More informationDistributed Coordination Algorithms for Multiple Fractional-Order Systems
Proceedings of the 47th IEEE onference on Decision and ontrol ancun, Mexico, Dec. 9-, 8 Distributed oordination Algorithms for Multiple Fractional-Order Systems Yongcan ao, Yan Li, Wei Ren, YangQuan hen
More informationConsensus of Multi-Agent Systems with
Consensus of Multi-Agent Systems with 1 General Linear and Lipschitz Nonlinear Dynamics Using Distributed Adaptive Protocols arxiv:1109.3799v1 [cs.sy] 17 Sep 2011 Zhongkui Li, Wei Ren, Member, IEEE, Xiangdong
More informationDistributed Formation Control While Preserving Connectedness
Proceedings of the 45th IEEE Conference on Decision & Control Manchester Grand Hyatt Hotel San Diego, CA, USA, December 13-15, 2006 Distributed Formation Control While Preserving Connectedness Meng Ji
More informationConsensus, Flocking and Opinion Dynamics
Consensus, Flocking and Opinion Dynamics Antoine Girard Laboratoire Jean Kuntzmann, Université de Grenoble antoine.girard@imag.fr International Summer School of Automatic Control GIPSA Lab, Grenoble, France,
More informationEffective Sensing Regions and Connectivity of Agents Undergoing Periodic Relative Motions
Effective Sensing Regions and Connectivity of Agents Undergoing Periodic Relative Motions Daniel T. Swain, Ming Cao, Naomi Ehrich Leonard Abstract Time-varying graphs are widely used to model communication
More informationThe Multi-Agent Rendezvous Problem - The Asynchronous Case
43rd IEEE Conference on Decision and Control December 14-17, 2004 Atlantis, Paradise Island, Bahamas WeB03.3 The Multi-Agent Rendezvous Problem - The Asynchronous Case J. Lin and A.S. Morse Yale University
More informationCoordinated Path Following for Mobile Robots
Coordinated Path Following for Mobile Robots Kiattisin Kanjanawanishkul, Marius Hofmeister, and Andreas Zell University of Tübingen, Department of Computer Science, Sand 1, 7276 Tübingen Abstract. A control
More informationInformation Consensus and its Application in Multi-vehicle Cooperative Control
Brigham Young University BYU ScholarsArchive All Faculty Publications 2007-07-01 Information Consensus and its Application in Multi-vehicle Cooperative Control Ella Atkins Randal Beard beard@byu.edu See
More informationFurther Results on the Stability of Distance-Based Multi- Robot Formations
Further Results on the Stability of Distance-Based Multi- Robot Formations The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation
More informationDistributed Formation Control without a Global Reference Frame
2014 American Control Conference (ACC) June 4-6, 2014. Portland, Oregon, USA Distributed Formation Control without a Global Reference Frame Eduardo Montijano, Dingjiang Zhou, Mac Schwager and Carlos Sagues
More informationCooperative Control Synthesis for Moving-Target-Enclosing with Changing Topologies
2010 IEEE International Conference on Robotics and Automation Anchorage Convention District May 3-8, 2010, Anchorage, Alaska, USA Cooperative Control Synthesis for Moving-Target-Enclosing with Changing
More informationThe abundance of embedded computational
Information Consensus in Multivehicle Cooperative Control WEI REN, RANDAL W. BEARD, and ELLA M. ATKINS COLLECTIVE GROUP BEHAVIOR THROUGH LOCAL INTERACTION The abundance of embedded computational resources
More information