912 research outputs found
Event-Triggered Algorithms for Leader-Follower Consensus of Networked Euler-Lagrange Agents
This paper proposes three different distributed event-triggered control
algorithms to achieve leader-follower consensus for a network of Euler-Lagrange
agents. We firstly propose two model-independent algorithms for a subclass of
Euler-Lagrange agents without the vector of gravitational potential forces. By
model-independent, we mean that each agent can execute its algorithm with no
knowledge of the agent self-dynamics. A variable-gain algorithm is employed
when the sensing graph is undirected; algorithm parameters are selected in a
fully distributed manner with much greater flexibility compared to all previous
work concerning event-triggered consensus problems. When the sensing graph is
directed, a constant-gain algorithm is employed. The control gains must be
centrally designed to exceed several lower bounding inequalities which require
limited knowledge of bounds on the matrices describing the agent dynamics,
bounds on network topology information and bounds on the initial conditions.
When the Euler-Lagrange agents have dynamics which include the vector of
gravitational potential forces, an adaptive algorithm is proposed which
requires more information about the agent dynamics but can estimate uncertain
agent parameters.
For each algorithm, a trigger function is proposed to govern the event update
times. At each event, the controller is updated, which ensures that the control
input is piecewise constant and saves energy resources. We analyse each
controllers and trigger function and exclude Zeno behaviour. Extensive
simulations show 1) the advantages of our proposed trigger function as compared
to those in existing literature, and 2) the effectiveness of our proposed
controllers.Comment: Extended manuscript of journal submission, containing omitted proofs
and simulation
Distributed model-independent consensus of Euler-Lagrange agents on directed networks
This paper proposes a distributed model-independent algorithm to achieve leaderless consensus on a directed network where each fully-actuated agent has self-dynamics described by Euler–Lagrange equations of motion. Specifically, we aim to achieve consensus of the generalised coordinates with zero generalised velocity. We show that on a strongly connected graph, a model-independent algorithm can achieve the consensus objective at an exponential rate if an upper bound on the initial conditions is known a priori. By model-independent, we mean that each agent can execute the algorithm with no knowledge of the equations describing the self-dynamics of any agent. For design of the control laws which achieve consensus, a control gain scalar and a control gain matrix are required to satisfy several inequalities involving bounds on the matrices of the agent dynamic model, bounds on the Laplacian matrix describing the network topology and the set of initial conditions; design of the algorithm therefore requires some knowledge on the bounds of the agent dynamical parameters. Because only bounds are required, the proposed algorithm offers robustness to uncertainty in the parameters of the multiagent system. We systematically show that additional relative velocity information improves the performance of the controller. Numerical simulations are provided to show the effectiveness of the algorithm.This work was supported by the National Natural Science Foundation of China (grant
61375072), and by Data61-CSIRO (formerly NICTA)
Attitude Synchronization of Spacecraft Formation with Optimization and Adaptation of Consensus Penalty Terms
The contribution of this thesis is on the temporal adjustment of the consensus weights, as applied to spacecraft formation control. Such an objective is attained by dynamically enforcing attitude synchronization via coupling terms included in each spacecraft controller. It is assumed that each spacecraft has identical dynamics but with unknown inertia parameters and external disturbances. By augmenting a standard adaptive controller that accounts for the unknown parameters, made feasible via an assumption on parameterization, with adaptation of the consensus weights, one opts to improve spacecraft synchronization. The coupling terms, responsible for enforcing synchronization amongst spacecraft, are weighted dynamically in proportion to the disagreement between the states of the spacecraft. The time adjustment of edge-dependent gains as well as the special cases of node-dependent and agent-independent constant gains are derived using Lyapunov redesign methods. The proposed adaptive control architectures which allow for adaptation of both parameter uncertainties and consensus penalty terms are demonstrated via extensive numerical studies of spacecraft networks with limited connectivity. By considering the sum of deviation-from-the-mean and rotational kinetic energy as appropriate metrics for synchronization and controller performance, the numerical studies also provide insights on the choice of optimal consensus gains
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