20 research outputs found
An Observer-based Longitudinal Control of Car-like Vehicles Platoon Navigating in an Urban Environment
International audienceIn this paper, we study longitudinal motion controlof car-like vehicles platoon navigating in an urban environmentwith minimum communication links. To achieve a higher trafficflow, a constant-spacing policy between successive vehicles iscommonly used but this is at a cost of an increased number ofcommunication links as any vehicle information must broadcastto all its followers. Therefore, we propose a distributed observer-based control law that depends both on communicated andmeasured information. Our formulation allows designing thecontrol law directly in the curvilinear coordinates. Internal andstring stability analysis are conducted. We provide simulationresults, through dynamic vehicular mobility simulator, to illus-trate the feasibility of the proposed approach and corroborate our theoretical findings
An Observer-based Longitudinal Control of Car-like Vehicles Platoon Navigating in an Urban Environment
International audienceIn this paper, we study longitudinal motion controlof car-like vehicles platoon navigating in an urban environmentwith minimum communication links. To achieve a higher trafficflow, a constant-spacing policy between successive vehicles iscommonly used but this is at a cost of an increased number ofcommunication links as any vehicle information must broadcastto all its followers. Therefore, we propose a distributed observer-based control law that depends both on communicated andmeasured information. Our formulation allows designing thecontrol law directly in the curvilinear coordinates. Internal andstring stability analysis are conducted. We provide simulationresults, through dynamic vehicular mobility simulator, to illus-trate the feasibility of the proposed approach and corroborate our theoretical findings
Stabilization of Networked Control Systems with Sparse Observer-Controller Networks
In this paper we provide a set of stability conditions for linear
time-invariant networked control systems with arbitrary topology, using a
Lyapunov direct approach. We then use these stability conditions to provide a
novel low-complexity algorithm for the design of a sparse observer-based
control network. We employ distributed observers by employing the output of
other nodes to improve the stability of each observer dynamics. To avoid
unbounded growth of controller and observer gains, we impose bounds on their
norms. The effects of relaxation of these bounds is discussed when trying to
find the complete decentralization conditions
Output consensus of nonlinear multi-agent systems with unknown control directions
In this paper, we consider an output consensus problem for a general class of
nonlinear multi-agent systems without a prior knowledge of the agents' control
directions. Two distributed Nussbaumtype control laws are proposed to solve the
leaderless and leader-following adaptive consensus for heterogeneous multiple
agents. Examples and simulations are given to verify their effectivenessComment: 10 pages;2 figure
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
Self-Triggered and Event-Triggered Set-Valued Observers
This paper addresses the problem of reducing the required network load and computational power for the implementation of Set-Valued Observers (SVOs) in Networked Control System (NCS). Event- and self-triggered strategies for NCS, modeled as discrete-time Linear Parameter-Varying (LPV) systems, are studied by showing how the triggering condition can be selected. The methodology provided can be applied to determine when it is required to perform a full (``classical'') computation of the SVOs, while providing low-complexity state overbounds for the remaining time, at the expenses of temporarily reducing the estimation accuracy. As part of the procedure, an algorithm is provided to compute a suitable centrally symmetric polytope that allows to find hyper-parallelepiped and ellipsoidal overbounds to the exact set-valued state estimates calculated by the SVOs. By construction, the proposed triggering techniques do not influence the convergence of the SVOs, as at some subsequent time instants, set-valued estimates are computed using the \emph{conventional} SVOs. Results are provided for the triggering frequency of the self-triggered strategy and two interesting cases: distributed systems when the dynamics of all nodes are equal up to a reordering of the matrix; and when the probability distribution of the parameters influencing the dynamics is known. The performance of the proposed algorithm is demonstrated in simulation by using a time-sensitive example