3 research outputs found
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 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