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Distributed leader-follower flocking control for multi-agent dynamical systems with time-varying velocities

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A distributed leader-follower flocking algorithm for multi-agent dynamical systems with time-varying velocities is developed where each agent is governed by second-order dynamics.
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This article is published in Systems & Control Letters.The article was published on 2010-09-01 and is currently open access. It has received 250 citations till now. The article focuses on the topics: Leader election & Distributed algorithm.

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An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination

TL;DR: In this article, the authors reviewed some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006 and proposed several promising research directions along with some open problems that are deemed important for further investigations.
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An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

TL;DR: In this paper, the authors reviewed some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006, and proposed several promising research directions along with some open problems that are deemed important for further investigations.
Journal ArticleDOI

Synchronization via pinning control on general complex networks

TL;DR: This paper studies synchronization via pinning control on general complex dynamical networks, such as strongly connected networks, networks with a directed spanning tree, weaklyconnected networks, and directed forests, finding that the strongly connected components with very few connections from other components should be controlled and the components with many connections fromother components can achieve synchronization even without controls.
Journal ArticleDOI

Second-order consensus for multi-agent systems with switching topology and communication delay

TL;DR: This paper investigates two kinds of consensus problems for second-order agents under directed and arbitrarily switching topologies, that is, the cases without and with communication delay, and shows that consensus can be reached if the delay is small enough.
Journal ArticleDOI

Consensus of high-order multi-agent systems with large input and communication delays

TL;DR: It is shown that, if the delays are constant and exactly known, the consensus problems can be solved by both full state feedback and observer based output feedback protocols for arbitrarily large yet bounded delays.
References
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Journal ArticleDOI

Consensus problems in networks of agents with switching topology and time-delays

TL;DR: A distinctive feature of this work is to address consensus problems for networks with directed information flow by establishing a direct connection between the algebraic connectivity of the network and the performance of a linear consensus protocol.
Book

Optimization and nonsmooth analysis

TL;DR: The Calculus of Variations as discussed by the authors is a generalization of the calculus of variations, which is used in many aspects of analysis, such as generalized gradient descent and optimal control.
Book

Algebraic Graph Theory

TL;DR: The Laplacian of a Graph and Cuts and Flows are compared to the Rank Polynomial.
Journal ArticleDOI

Coordination of groups of mobile autonomous agents using nearest neighbor rules

TL;DR: A theoretical explanation for the observed behavior of the Vicsek model, which proves to be a graphic example of a switched linear system which is stable, but for which there does not exist a common quadratic Lyapunov function.
Book

Topics in Matrix Analysis

TL;DR: The field of values as discussed by the authors is a generalization of the field of value of matrices and functions, and it includes singular value inequalities, matrix equations and Kronecker products, and Hadamard products.
Related Papers (5)
Frequently Asked Questions (14)
Q1. What is the key to consensus in multi-agent dynamical systems?

Based on the algebraic graph theory [27,28], it has been shown that network connectivity is the key in reaching consensus [1,2,4,5,7,8]. 

Three heuristic rules were considered by Reynolds to animate flocking behavior: (1) velocity consensus, (2) center cohesion, and (3) collisionavoidance. 

In this paper, a distributed leader–follower flocking algorithm for multi-agent dynamical systems has been developed and analyzed, which considers the case in which the group has one virtual leader and the asymptotic velocity is time-varying. 

It has been the goal of this paper to analyze different flocking algorithms by using tools fromnonsmooth analysis in combination with ideas from the study of synchronization in complex systems. 

Rn are its position vector and velocity vector, respectively, and f (ro, vo) is the input vectorwhich governs the dynamics of the leader. 

In distributed flocking algorithms, the following control input is of particular interest [20–22,24–26]:ui(t) = f (ri, vi)−∇riV (r)+ c ∑ j∈Ni aij(‖rj − ri‖)(vj − vi)+ f̃i, (3)where the first term is a nonlinear dynamical term, the second term is a gradient-based term, V is the collective potential function to be defined, the third term is the velocity consensus term, f̃i is the navigation feedback based on information about the leader, Ni = {j ∈ V : ‖rj−ri‖ ≤ rs, j 6= i} denotes the neighbors of agent i, rs is the interaction range, and c is the coupling strength of velocity. 

As validated by simulations and experiments, flocking can be achieved in multi-agent dynamical systems by various distributed algorithms.∗ 

In addition, as long as thevelocity vector of the leader can be observed, all the agents in the group can move with the same velocity as the leader. 

It was pointed out [20] that the local minimum of V (r) is an α-lattice and vice versa, which is responsible for collision avoidance and cohesion in the group. 

Suppose E ⊂ Rm. A map x → F(x) is called a set-valued map from E → Rm, if each point x in a set E ⊂ Rm corresponds to a non-empty set F(x) ⊂ Rm. 

The generalized directional derivative of g at x in the direction w, denoted by g0(x;w), is defined asg0(x;w) ≡ lim y→x,t↓0sup g(y+ tw)− g(y)t . 

In order to embody the three Reynolds’ rules, Tanner et al. designed flocking algorithms in [24,25], where a collective potential function and a velocity consensus term were introduced. 

It has been proved that although each informed agent can only obtain partial information about the leader, the velocity of the whole group converges to that of the leader and the centroid of those informed agents, having the leader’s position information, follows the trajectory of the leader asymptotically. 

The classical directional derivative of−Vij at rs is given by−V ′ij(rs;w) = limt↓0 −Vij(rs + tw)− (−Vij(rs)) t .Ifw ≥ 0, then−V ′ij(rs;w) = limt↓0 Vij(rs)−