1,989 research outputs found
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach
Distributed Differential Graphical Game for Control of Double-Integrator Multi-Agent Systems with Input Delay
This paper studies cooperative control of noncooperative double-integrator
multi-agent systems (MASs) with input delay on connected directed graphs in the
context of a differential graphical game (DGG). In the distributed DGG, each
agent seeks a distributed information control policy by optimizing an
individual local performance index (PI) of distributed information from its
graph neighbors. The local PI, which quadratically penalizes the agent's
deviations from cooperative behavior (e.g., the consensus here), is constructed
through the use of the graph Laplacian matrix. For DGGs for double-integrator
MASs, the existing body of literature lacks the explicit characterization of
Nash equilibrium actions and their associated state trajectories with
distributed information. To address this issue, we first convert the N-player
DGG with m communication links into m coupled optimal control problems (OCPs),
which, in turn, convert to the two-point boundary-value problem (TPBVP). We
derive the explicit solutions for the TPBV that constitute the explicit
distributed information expressions for Nash equilibrium actions and the state
trajectories associated with them for the DGG. An illustrative example verifies
the explicit solutions of local information to achieve fully distributed
consensus.Comment: The revised version is accepted for publication in IEEE Transactions
on Control of Network System
Cooperative optimal preview tracking for linear descriptor multi-agent systems
© 2018 The Franklin Institute. In this paper, a cooperative optimal preview tracking problem is considered for continuous-time descriptor multi-agent systems with a directed topology containing a spanning tree. By the acyclic assumption and state augmentation technique, it is shown that the cooperative tracking problem is equivalent to local optimal regulation problems of a set of low-dimensional descriptor augmented subsystems. To design distributed optimal preview controllers, restricted system equivalent (r.s.e.) and preview control theory are first exploited to obtain optimal preview controllers for reduced-order normal subsystems. Then, by using the invertibility of restricted equivalent relations, a constructive method for designing distributed controller is presented which also yields an explicit admissible solution for the generalized algebraic Riccati equation. Sufficient conditions for achieving global cooperative preview tracking are proposed proving that the distributed controllers are able to stabilize the descriptor augmented subsystems asymptotically. Finally, the validity of the theoretical results is illustrated via numerical simulation
Distributed Linear Quadratic Optimal Control: Compute Locally and Act Globally
In this paper we consider the distributed linear quadratic control problem
for networks of agents with single integrator dynamics. We first establish a
general formulation of the distributed LQ problem and show that the optimal
control gain depends on global information on the network. Thus, the optimal
protocol can only be computed in a centralized fashion. In order to overcome
this drawback, we propose the design of protocols that are computed in a
decentralized way. We will write the global cost functional as a sum of local
cost functionals, each associated with one of the agents. In order to achieve
'good' performance of the controlled network, each agent then computes its own
local gain, using sampled information of its neighboring agents. This
decentralized computation will only lead to suboptimal global network behavior.
However, we will show that the resulting network will reach consensus. A
simulation example is provided to illustrate the performance of the proposed
protocol.Comment: 7 pages, 2 figure
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