23,885 research outputs found
An Optimal Transmission Strategy for Kalman Filtering over Packet Dropping Links with Imperfect Acknowledgements
This paper presents a novel design methodology for optimal transmission
policies at a smart sensor to remotely estimate the state of a stable linear
stochastic dynamical system. The sensor makes measurements of the process and
forms estimates of the state using a local Kalman filter. The sensor transmits
quantized information over a packet dropping link to the remote receiver. The
receiver sends packet receipt acknowledgments back to the sensor via an
erroneous feedback communication channel which is itself packet dropping. The
key novelty of this formulation is that the smart sensor decides, at each
discrete time instant, whether to transmit a quantized version of either its
local state estimate or its local innovation. The objective is to design
optimal transmission policies in order to minimize a long term average cost
function as a convex combination of the receiver's expected estimation error
covariance and the energy needed to transmit the packets. The optimal
transmission policy is obtained by the use of dynamic programming techniques.
Using the concept of submodularity, the optimality of a threshold policy in the
case of scalar systems with perfect packet receipt acknowledgments is proved.
Suboptimal solutions and their structural results are also discussed. Numerical
results are presented illustrating the performance of the optimal and
suboptimal transmission policies.Comment: Conditionally accepted in IEEE Transactions on Control of Network
System
Robust fault detection for networked systems with distributed sensors
Copyright [2011] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper is concerned with the robust fault detection problem for a class of discrete-time networked systems with distributed sensors. Since the bandwidth of the communication channel is limited, packets from different sensors may be dropped with different missing rates during the transmission. Therefore, a diagonal matrix is introduced to describe the multiple packet dropout phenomenon and the parameter uncertainties are supposed to reside in a polytope. The aim is to design a robust fault detection filter such that, for all probabilistic packet dropouts, all unknown inputs and admissible uncertain parameters, the error between the residual (generated by the fault detection filter) and the fault signal is made as small as possible. Two parameter-dependent approaches are proposed to obtain less conservative results. The existence of the desired fault detection filter can be determined from the feasibility of a set of linear matrix inequalities that can be easily solved by the efficient convex optimization method. A simulation example on a networked three-tank system is provided to illustrate the effectiveness and applicability of the proposed techniques.This work was supported by national 973 project under Grants 2009CB320602 and 2010CB731800, and the NSFC under Grants
60721003 and 60736026
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
Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks
Recent advances in electronics are enabling substantial processing to be
performed at each node (robots, sensors) of a networked system. Local
processing enables data compression and may mitigate measurement noise, but it
is still slower compared to a central computer (it entails a larger
computational delay). However, while nodes can process the data in parallel,
the centralized computational is sequential in nature. On the other hand, if a
node sends raw data to a central computer for processing, it incurs
communication delay. This leads to a fundamental communication-computation
trade-off, where each node has to decide on the optimal amount of preprocessing
in order to maximize the network performance. We consider a network in charge
of estimating the state of a dynamical system and provide three contributions.
First, we provide a rigorous problem formulation for optimal real-time
estimation in processing networks in the presence of delays. Second, we show
that, in the case of a homogeneous network (where all sensors have the same
computation) that monitors a continuous-time scalar linear system, the optimal
amount of local preprocessing maximizing the network estimation performance can
be computed analytically. Third, we consider the realistic case of a
heterogeneous network monitoring a discrete-time multi-variate linear system
and provide algorithms to decide on suitable preprocessing at each node, and to
select a sensor subset when computational constraints make using all sensors
suboptimal. Numerical simulations show that selecting the sensors is crucial.
Moreover, we show that if the nodes apply the preprocessing policy suggested by
our algorithms, they can largely improve the network estimation performance.Comment: 15 pages, 16 figures. Accepted journal versio
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