7,264 research outputs found
Distributed Event-Based State Estimation for Networked Systems: An LMI-Approach
In this work, a dynamic system is controlled by multiple sensor-actuator
agents, each of them commanding and observing parts of the system's input and
output. The different agents sporadically exchange data with each other via a
common bus network according to local event-triggering protocols. From these
data, each agent estimates the complete dynamic state of the system and uses
its estimate for feedback control. We propose a synthesis procedure for
designing the agents' state estimators and the event triggering thresholds. The
resulting distributed and event-based control system is guaranteed to be stable
and to satisfy a predefined estimation performance criterion. The approach is
applied to the control of a vehicle platoon, where the method's trade-off
between performance and communication, and the scalability in the number of
agents is demonstrated.Comment: This is an extended version of an article to appear in the IEEE
Transactions on Automatic Control (additional parts in the Appendix
Recommended from our members
L-2 State Estimation With Guaranteed Convergence Speed in the Presence of Sporadic Measurements
This paper deals with the problem of estimating the state of a nonlinear time-invariant system in the presence of sporadically available measurements and external perturbations. An observer with a continuous intersample injection term is proposed. Such an intersample injection is provided by a linear dynamical system, whose state is reset to the measured output estimation error whenever a new measurement is available. The resulting system is augmented with a timer triggering the arrival of a new measurement and analyzed in a hybrid system framework. The design of the observer is performed to achieve exponential convergence with a given decay rate of the estimation error. Robustness with respect to external perturbations and L2-external stability from plant perturbations to a given performance output are considered. Computationally efficient algorithms based on the solution to linear matrix inequalities are proposed to design the observer. Finally, the effectiveness of the proposed methodology is shown in an example
Event-based State Estimation: An Emulation-based Approach
An event-based state estimation approach for reducing communication in a
networked control system is proposed. Multiple distributed sensor agents
observe a dynamic process and sporadically transmit their measurements to
estimator agents over a shared bus network. Local event-triggering protocols
ensure that data is transmitted only when necessary to meet a desired
estimation accuracy. The event-based design is shown to emulate the performance
of a centralised state observer design up to guaranteed bounds, but with
reduced communication. The stability results for state estimation are extended
to the distributed control system that results when the local estimates are
used for feedback control. Results from numerical simulations and hardware
experiments illustrate the effectiveness of the proposed approach in reducing
network communication.Comment: 21 pages, 8 figures, this article is based on the technical report
arXiv:1511.05223 and is accepted for publication in IET Control Theory &
Application
Event-triggered Learning
The efficient exchange of information is an essential aspect of intelligent
collective behavior. Event-triggered control and estimation achieve some
efficiency by replacing continuous data exchange between agents with
intermittent, or event-triggered communication. Typically, model-based
predictions are used at times of no data transmission, and updates are sent
only when the prediction error grows too large. The effectiveness in reducing
communication thus strongly depends on the quality of the prediction model. In
this article, we propose event-triggered learning as a novel concept to reduce
communication even further and to also adapt to changing dynamics. By
monitoring the actual communication rate and comparing it to the one that is
induced by the model, we detect a mismatch between model and reality and
trigger model learning when needed. Specifically, for linear Gaussian dynamics,
we derive different classes of learning triggers solely based on a statistical
analysis of inter-communication times and formally prove their effectiveness
with the aid of concentration inequalities
LMI-Based Reset Unknown Input Observer for State Estimation of Linear Uncertain Systems
This paper proposes a novel kind of Unknown Input Observer (UIO) called Reset
Unknown Input Observer (R-UIO) for state estimation of linear systems in the
presence of disturbance using Linear Matrix Inequality (LMI) techniques. In
R-UIO, the states of the observer are reset to the after-reset value based on
an appropriate reset law in order to decrease the norm and settling time
of estimation error. It is shown that the application of the reset theory to
the UIOs in the LTI framework can significantly improve the transient response
of the observer. Moreover, the devised approach can be applied to both SISO and
MIMO systems. Furthermore, the stability and convergence analysis of the
devised R-UIO is addressed. Finally, the efficiency of the proposed method is
demonstrated by simulation results
- …