1,278 research outputs found
An Optimal Control Derivation of Nonlinear Smoothing Equations
The purpose of this paper is to review and highlight some connections between
the problem of nonlinear smoothing and optimal control of the Liouville
equation. The latter has been an active area of recent research interest owing
to work in mean-field games and optimal transportation theory. The nonlinear
smoothing problem is considered here for continuous-time Markov processes. The
observation process is modeled as a nonlinear function of a hidden state with
an additive Gaussian measurement noise. A variational formulation is described
based upon the relative entropy formula introduced by Newton and Mitter. The
resulting optimal control problem is formulated on the space of probability
distributions. The Hamilton's equation of the optimal control are related to
the Zakai equation of nonlinear smoothing via the log transformation. The
overall procedure is shown to generalize the classical Mortensen's minimum
energy estimator for the linear Gaussian problem.Comment: 7 pages, 0 figures, under peer reviewin
Joint Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications
This paper introduces a novel feedback-control based particle filter for the
solution of the filtering problem with data association uncertainty. The
particle filter is referred to as the joint probabilistic data
association-feedback particle filter (JPDA-FPF). The JPDA-FPF is based on the
feedback particle filter introduced in our earlier papers. The remarkable
conclusion of our paper is that the JPDA-FPF algorithm retains the innovation
error-based feedback structure of the feedback particle filter, even with data
association uncertainty in the general nonlinear case. The theoretical results
are illustrated with the aid of two numerical example problems drawn from
multiple target tracking applications.Comment: In Proc. of the 2012 American Control Conferenc
Stability Margin Scaling Laws for Distributed Formation Control as a Function of Network Structure
We consider the problem of distributed formation control of a large number of
vehicles. An individual vehicle in the formation is assumed to be a fully
actuated point mass. A distributed control law is examined: the control action
on an individual vehicle depends on (i) its own velocity and (ii) the relative
position measurements with a small subset of vehicles (neighbors) in the
formation. The neighbors are defined according to an information graph.
In this paper we describe a methodology for modeling, analysis, and
distributed control design of such vehicular formations whose information graph
is a D-dimensional lattice. The modeling relies on an approximation based on a
partial differential equation (PDE) that describes the spatio-temporal
evolution of position errors in the formation. The analysis and control design
is based on the PDE model. We deduce asymptotic formulae for the closed-loop
stability margin (absolute value of the real part of the least stable
eigenvalue) of the controlled formation. The stability margin is shown to
approach 0 as the number of vehicles N goes to infinity. The exponent on the
scaling law for the stability margin is influenced by the dimension and the
structure of the information graph. We show that the scaling law can be
improved by employing a higher dimensional information graph.
Apart from analysis, the PDE model is used for a mistuning-based design of
control gains to maximize the stability margin. Mistuning here refers to small
perturbation of control gains from their nominal symmetric values. We show that
the mistuned design can have a significantly better stability margin even with
a small amount of perturbation. The results of the analysis with the PDE model
are corroborated with numerical computation of eigenvalues with the state-space
model of the formation.Comment: This paper is the expanded version of the paper with the same name
which is accepted by the IEEE Transactions on Automatic Control. The final
version is updated on Oct. 12, 201
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