11,323 research outputs found
Distributed Robust Dynamic Average Consensus with Dynamic Event-Triggered Communication
This paper presents the formulation and analysis of a fully distributed
dynamic event-triggered communication based robust dynamic average consensus
algorithm. Dynamic average consensus problem involves a networked set of agents
estimating the time-varying average of dynamic reference signals locally
available to individual agents. We propose an asymptotically stable solution to
the dynamic average consensus problem that is robust to network disruptions.
Since this robust algorithm requires continuous communication among agents, we
introduce a novel dynamic event-triggered communication scheme to reduce the
overall inter-agent communications. It is shown that the event-triggered
algorithm is asymptotically stable and free of Zeno behavior. Numerical
simulations are provided to illustrate the effectiveness of the proposed
algorithm
Distributed average tracking for multiple reference signals with general linear dynamics
This technical note studies the distributed average tracking problem for
multiple time-varying signals with general linear dynamics, whose reference
inputs are nonzero and not available to any agent in the network. In
distributed fashion, a pair of continuous algorithms with, respectively, static
and adaptive coupling strengths are designed. Based on the boundary layer
concept, the proposed continuous algorithm with static coupling strengths can
asymptotically track the average of the multiple reference signals without
chattering phenomenon. Furthermore, for the case of algorithms with adaptive
coupling strengths, the average tracking errors are uniformly ultimately
bounded and exponentially converge to a small adjustable bounded set. Finally,
a simulation example is presented to show the validity of the theoretical
results
Distributed Average Tracking for Double-integrator Multi-agent Systems with Reduced Requirement on Velocity Measurements
This paper addresses distributed average tracking for a group of physical
double-integrator agents under an undirected graph with reduced requirement on
velocity measurements. The idea is that multiple agents track the average of
multiple time-varying input signals, each of which is available to only one
agent, under local interaction with neighbors. We consider two cases. First, a
distributed discontinuous algorithm and filter are proposed, where each agent
needs the relative positions between itself and its neighbors and its
neighbors' filter outputs obtained through communication but the requirement
for either absolute or relative velocity measurements is removed. The agents'
positions and velocities must be initialized correctly, but the algorithm can
deal with a wide class of input signals with bounded acceleration deviations.
Second, a distributed discontinuous algorithm and filter are proposed to remove
the requirement for communication and accurate initialization. Here each agent
needs to measure the relative position between itself and its neighbors and its
own velocity but the requirement for relative velocity measurements between
itself and its neighbors is removed. The algorithm can deal with the case where
the input signals and their velocities and accelerations are all bounded.
Numerical simulations are also presented to illustrate the theoretical results
Distributed Average Tracking for Second-order Agents with Nonlinear Dynamics
This paper addresses distributed average tracking of physical second-order
agents with nonlinear dynamics, where the interaction among the agents is
described by an undirected graph. In both agents' and reference inputs'
dynamics, there is a nonlinear term that satisfying the Lipschitz-type
condition. To achieve the distributed average tracking problem in the presence
of nonlinear term, a non-smooth filter and a control input are designed for
each agent. The idea is that each filter outputs converge to the average of the
reference inputs and the reference velocities asymptotically and in parallel
each agent's position and velocity are driven to track its filter outputs. To
overcome the nonlinear term unboundedness effect, novel state-dependent time
varying gains are employed in each agent's filter and control input. In the
proposed algorithm, each agent needs its neighbors' filters outputs besides its
own filter outputs, absolute position and absolute velocity and its neighbors'
reference inputs and reference velocities. Finally, the algorithm is simplified
to achieve the distributed average tracking of physical second-order agents in
the presence of an unknown bounded term in both agents' and reference inputs'
dynamics.Comment: 6 pages, conferenc
Distributed Average Tracking of Heterogeneous Physical Second-order Agents With No Input Signals Constraint
This paper addresses distributed average tracking of physical second-order
agents with heterogeneous nonlinear dynamics, where there is no constraint on
input signals. The nonlinear terms in agents' dynamics are heterogeneous,
satisfying a Lipschitz-like condition that will be defined later and is more
general than the Lipschitz condition. In the proposed algorithm, a control
input and a filter are designed for each agent. Each agent's filter has two
outputs and the idea is that the first output estimates the average of the
input signals and the second output estimates the average of the input
velocities asymptotically. In parallel, each agent's position and velocity are
driven to track, respectively, the first and the second outputs. Having
heterogeneous nonlinear terms in agents' dynamics necessitates designing the
filters for agents. Since the nonlinear terms in agents' dynamics can be
unbounded and the input signals are arbitrary, novel state-dependent
time-varying gains are employed in agents' filters and control inputs to
overcome these unboundedness effects. Finally the results are improved to
achieve the distributed average tracking for a group of double-integrator
agents, where there is no constraint on input signals and the filter is not
required anymore. Numerical simulations are also presented to illustrate the
theoretical results
Distributed Average Tracking for Lipschitz-Type Nonlinear Dynamical Systems
In this paper, a distributed average tracking problem is studied for
Lipschitz-type nonlinear dynamical systems. The objective is to design
distributed average tracking algorithms for locally interactive agents to track
the average of multiple reference signals. Here, in both the agents' and the
reference signals' dynamics, there is a nonlinear term satisfying the
Lipschitz-type condition. Three types of distributed average tracking
algorithms are designed. First, based on state-dependent-gain designing
approaches, a robust distributed average tracking algorithm is developed to
solve distributed average tracking problems without requiring the same initial
condition. Second, by using a gain adaption scheme, an adaptive distributed
average tracking algorithm is proposed in this paper to remove the requirement
that the Lipschitz constant is known for agents. Third, to reduce chattering
and make the algorithms easier to implement, a continuous distributed average
tracking algorithm based on a time-varying boundary layer is further designed
as a continuous approximation of the previous discontinuous distributed average
tracking algorithms
Distributed Average Tracking for Multiple Signals Generated by Linear Dynamical Systems: An Edge-based Framework
This paper studies the distributed average tracking problem for multiple
time-varying signals generated by linear dynamics, whose reference inputs are
nonzero and not available to any agent in the network. In the edge-based
framework, a pair of continuous algorithms with, respectively, static and
adaptive coupling strengths are designed. Based on the boundary layer concept,
the proposed continuous algorithm with static coupling strengths can
asymptotically track the average of multiple reference signals without the
chattering phenomenon. Furthermore, for the case of algorithms with adaptive
coupling strengths, average tracking errors are uniformly ultimately bounded
and exponentially converge to a small adjustable bounded set. Finally, a
simulation example is presented to show the validity of theoretical results.Comment: accepted in press, Automatica 2016. arXiv admin note: substantial
text overlap with arXiv:1312.744
Dynamic Average Consensus under Limited Control Authority and Privacy Requirements
This paper introduces a novel continuous-time dynamic average consensus
algorithm for networks whose interaction is described by a strongly connected
and weight-balanced directed graph. The proposed distributed algorithm allows
agents to track the average of their dynamic inputs with some steady-state
error whose size can be controlled using a design parameter. This steady-state
error vanishes for special classes of input signals. We analyze the asymptotic
correctness of the algorithm under time-varying interaction topologies and
characterize the requirements on the stepsize for discrete-time
implementations. We show that our algorithm naturally preserves the privacy of
the local input of each agent. Building on this analysis, we synthesize an
extension of the algorithm that allows individual agents to control their own
rate of convergence towards agreement and handle saturation bounds on the
driving command. Finally, we show that the proposed extension additionally
preserves the privacy of the transient response of the agreement states and the
final agreement value from internal and external adversaries. Numerical
examples illustrate the results.Comment: 44 page
Distributed Convex Optimization for Continuous-Time Dynamics with Time-Varying Cost Function
In this paper, a time-varying distributed convex optimization problem is
studied for continuous-time multi-agent systems. Control algorithms are
designed for the cases of single-integrator and double-integrator dynamics. Two
discontinuous algorithms based on the signum function are proposed to solve the
problem in each case. Then in the case of double-integrator dynamics, two
continuous algorithms based on, respectively, a time-varying and a fixed
boundary layer are proposed as continuous approximations of the signum
function. Also, to account for inter-agent collision for physical agents, a
distributed convex optimization problem with swarm tracking behavior is
introduced for both single-integrator and double-integrator dynamics
On Event Triggered Tracking for Nonlinear Systems
In this paper we study an event based control algorithm for trajectory
tracking in nonlinear systems. The desired trajectory is modelled as the
solution of a reference system with an exogenous input and it is assumed that
the desired trajectory and the exogenous input to the reference system are
uniformly bounded. Given a continuous-time control law that guarantees global
uniform asymptotic tracking of the desired trajectory, our algorithm provides
an event based controller that not only guarantees uniform ultimate boundedness
of the tracking error, but also ensures non-accumulation of inter-execution
times. In the case that the derivative of the exogenous input to the reference
system is also uniformly bounded, an arbitrarily small ultimate bound can be
designed. If the exogenous input to the reference system is piecewise
continuous and not differentiable everywhere then the achievable ultimate bound
is constrained and the result is local, though with a known region of
attraction. The main ideas in the paper are illustrated through simulations of
trajectory tracking by a nonlinear system.Comment: 8 pages, 3 figures. Includes proofs for all result
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