This paper analyzes distributed control protocols for first- and second-order networked dynamical systems. We propose a class of nonlinear consensus controllers where the input of each agent can be written as a product of a nonlinear gain, and a sum of nonlinear interaction functions. By using integral Lyapunov functions, we prove the stability of the proposed control protocols, and explicitly characterize the equilibrium set. We also propose a distributed proportional-integral (PI) controller for networked dynamical systems. The PI controllers successfully attenuate constant disturbances in the network. We prove that agents with single-integrator dynamics are stable for any integral gain, and give an explicit tight upper bound on the integral gain for when the system is stable for agents with double-integrator dynamics. Throughout the paper we highlight some possible applications of the proposed controllers by realistic simulations of autonomous satellites, power systems and building temperature control.
Nonlinear interaction functions for consensus problems are well-studied @cite_22 @cite_20 , in applications to, e.g., consensus with preserving connectedness @cite_30 and collision avoidance @cite_30 . Sufficient conditions for the convergence of nonlinear protocols for first-order integrator dynamics are given in @cite_28 . Consensus on a general nonlinear function value, referred to as @math -consensus, was studied in @cite_26 , by the use of nonlinear gain functions. The literature on @math -consensus has been focused on agents with single-integrator dynamics. However, as we show later, our results can be generalized to hold also for double-integrator dynamics. Consensus protocols where the input of an agent can be separated into a product of a positive function of the agents own state were studied in @cite_12 for single integrator dynamics. In @cite_13 , position consensus for agents with double-integrator dynamics under a class of nonlinear interaction functions and nonlinear velocity-damping is studied. In contrast to @cite_13 , we study undamped consensus protocols for single- and double-integrator dynamics using integral Lyapunov functions. In @cite_9 double-integrator consensus problems with linear non-homogeneous damping coefficients are considered. We generalize these results to also hold for a class of nonlinear damping coefficients.
@cite_30 @cite_26 @cite_22 @cite_28 @cite_9 @cite_13 @cite_12 @cite_20
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A distributed swarm aggregation algorithm is developed for a team of multiple kinematic agents. Specifically, each agent is assigned a control law, which is the sum of two elements: a repulsive potential field, which is responsible for the collision avoidance objective, and an attractive potential field, which forces the agents to converge to a configuration where they are close to each other. Furthermore, the attractive potential field forces the agents that are initially located within the sensing radius of an agent to remain within this area for all time. In this way, the connectivity properties of the initially formed communication graph are rendered invariant for the trajectories of the closed-loop system. It is shown that under the proposed control law, agents converge to a configuration where each agent is located at a bounded distance from each of its neighbors. The results are also extended to the case of nonholonomic kinematic unicycle-type agents and to the case of dynamic edge addition. In the latter case, we derive a smaller bound in the swarm size than in the static case. This paper presents analysis and design results for distributed consensus algorithms in multi-agent networks. We consider arbitrary consensus functions of the initial state of the network agents. Under mild smoothness assumptions, we obtain necessary and sufficient conditions characterizing any algorithm that asymptotically achieves consensus. This characterization is the building block to obtain various design results. We first identify a class of smooth functions for which one can synthesize in a systematic way distributed algorithms that achieve consensus. We apply this result to the family of weighted power mean functions, and characterize the exponential convergence properties of the resulting algorithms. We conclude with two distributed algorithms that achieve, respectively, max and min consensus in finite time. In this paper, we introduce linear and nonlinear consensus protocols for networks of dynamic agents that allow the agents to agree in a distributed and cooperative fashion. We consider the cases of networks with communication time-delays and channels that have filtering effects. We find a tight upper bound on the maximum fixed time-delay that can be tolerated in the network. It turns out that the connectivity of the network is the key in reaching a consensus. The case of agreement with bounded inputs is considered by analyzing the convergence of a class of nonlinear protocols. A Lyapunov function is introduced that quantifies the total disagreement among the nodes of a network. Simulation results are provided for agreement in networks with communication time-delays and constrained inputs. This paper is concerned with the convergence of a class of continuous-time nonlinear consensus algorithms for single integrator agents. In the consensus algorithms studied here, the control input of each agent is assumed to be a state-dependent combination of the relative positions of its neighbors in the information flow graph. Using a novel approach based on the smallest order of the nonzero derivative, it is shown that under some mild conditions the convex hull of the agents has a contracting property. A set-valued LaSalle-like approach is subsequently employed to show the convergence of the agents to a common point. The results are shown to be more general than the ones reported in the literature in some cases. An illustrative example demonstrates how the proposed convergence conditions can be verified. In this paper, the consensus problems for networks of dynamic agents are investigated. The agent dynamics is adopted as a typical point mass model based on the Newton's law. The average-consensus problem is proposed for such class of networks, which includes two aspects, the agreement of the states of the agents and the convergence to zero of the speeds of the agents. A linear consensus protocol for such networks is established for solving such a consensus problem that includes two parts, a local speed feedback controller and the interactions from the finite neighbours. Two kinds of topology are discussed: one is fixed topology, the other is switching one. The convergence analysis is proved and the protocol performance is discussed as well. The simulation results are presented that are consistent with our theoretical results. Copyright © 2006 John Wiley & Sons, Ltd. Robust static output-feedback controllers are designed that achieve consensus in networks of heterogeneous agents modeled as nonlinear systems of relative degree two. Both ideal communication networks and networks with communication constraints are considered, e.g., with limited communication range or heterogeneous communication delays. All design conditions that are presented are scalable to large and heterogeneous networks because the controller parameters depend only on the dynamics of the corresponding agent and its neighbors, but not on other agents in the network. We consider stationary consensus protocols for networks of dynamic agents with fixed topologies. At each time instant, each agent knows only its and its neighbors’ state, but must reach consensus on a group decision value that is function of all the agents’ initial state. We show that the agents can reach consensus if the value of such a function is time-invariant when computed over the agents’ state trajectories. We use this basic result to introduce a non-linear protocol design rule allowing consensus on a quite general set of values. Such a set includes, e.g., any generalized mean of order p of the agents’ initial states. As a second contribution we show that our protocol design is the solution of individual optimizations performed by the agents. This notion suggests a game theoretic interpretation of consensus problems as mechanism design problems. Under this perspective a supervisor entails the agents to reach a consensus by imposing individual objectives. We prove that such objectives can be chosen so that rational agents have a unique optimal protocol, and asymptotically reach consensus on a desired group decision value. We use a Lyapunov approach to prove that the asymptotical consensus can be reached when the communication links between nearby agents define a time-invariant undirected network. Finally we perform a simulation study concerning the vertical alignment maneuver of a team of unmanned air vehicles.