15 research outputs found
Moving horizon partition-based state estimation of large-scale systems -- Revised version
This report presents three Moving Horizon Estimation (MHE) methods for
discrete-time partitioned linear systems, i.e. systems decomposed into coupled
subsystems with non-overlapping states. The MHE approach is used due to its
capability of exploiting physical constraints on states in the estimation
process. In the proposed algorithms, each subsystem solves reduced-order MHE
problems to estimate its own state and different estimators have different
computational complexity, accuracy and transmission requirements among
subsystems. In all cases, conditions for the convergence of the estimation
error to zero are analyzed
Distributed bounded-error state estimation for partitioned systems based on practical robust positive invariance
We propose a partition-based state estimator for linear discrete-time systems
composed by coupled subsystems affected by bounded disturbances. The
architecture is distributed in the sense that each subsystem is equipped with a
local state estimator that exploits suitable pieces of information from parent
subsystems. Moreover, differently from methods based on moving horizon
estimation, our approach does not require the on-line solution to optimization
problems. Our state-estimation scheme, that is based on the notion of practical
robust positive invariance developed in Rakovic 2011, also guarantees
satisfaction of constraints on local estimation errors and it can be updated
with a limited computational effort when subsystems are added or removed
Distributed resilient filtering of large-scale systems with channel scheduling
summary:This paper addresses the distributed resilient filtering for discrete-time large-scale systems (LSSs) with energy constraints, where their information are collected by sensor networks with a same topology structure. As a typical model of information physics systems, LSSs have an inherent merit of modeling wide area power systems, automation processes and so forth. In this paper, two kinds of channels are employed to implement the information transmission in order to extend the service time of sensor nodes powered by energy-limited batteries. Specifically, the one has the merit of high reliability by sacrificing energy cost and the other reduces the energy cost but could result in packet loss. Furthermore, a communication scheduling matrix is introduced to govern the information transmission in these two kind of channels. In this scenario, a novel distributed filter is designed by fusing the compensated neighboring estimation. Then, two matrix-valued functions are derived to obtain the bounds of the covariance matrices of one-step prediction errors and the filtering errors. In what follows, the desired gain matrices are analytically designed to minimize the provided bounds with the help of the gradient-based approach and the mathematical induction. Furthermore, the effect on filtering performance from packet loss is profoundly discussed and it is claimed that the filtering performance becomes better when the probability of packet loss decreases. Finally, a simulation example on wide area power systems is exploited to check the usefulness of the designed distributed filter
Distributed Kalman Estimation with Decoupled Local Filters
We study a distributed Kalman filtering problem in which a number of nodes
cooperate without central coordination to estimate a common state based on
local measurements and data received from neighbors. This is typically done by
running a local filter at each node using information obtained through some
procedure for fusing data across the network. A common problem with existing
methods is that the outcome of local filters at each time step depends on the
data fused at the previous step. We propose an alternative approach to
eliminate this error propagation. The proposed local filters are guaranteed to
be stable under some mild conditions on certain global structural data, and
their fusion yields the centralized Kalman estimate. The main feature of the
new approach is that fusion errors introduced at a given time step do not carry
over to subsequent steps. This offers advantages in many situations including
when a global estimate in only needed at a rate slower than that of
measurements or when there are network interruptions. If the global structural
data can be fused correctly asymptotically, the stability of local filters is
equivalent to that of the centralized Kalman filter. Otherwise, we provide
conditions to guarantee stability and bound the resulting estimation error.
Numerical experiments are given to show the advantage of our method over other
existing alternatives