41,306 research outputs found
A Robust Continuous Time Fixed Lag Smoother for Nonlinear Uncertain Systems
This paper presents a robust fixed lag smoother for a class of nonlinear
uncertain systems. A unified scheme, which combines a nonlinear robust
estimator with a stable fixed lag smoother, is presented to improve the error
covariance of the estimation. The robust fixed lag smoother is based on the use
of Integral Quadratic Constraints and minimax LQG control. The state estimator
uses a copy of the system nonlinearity in the estimator and combines an
approximate model of the delayed states to produce a smoothed signal. In order
to see the effectiveness of the method, it is applied to a quantum optical
phase estimation problem. Results show significant improvement in the error
covariance of the estimator using fixed lag smoother in the presence of
nonlinear uncertainty.Comment: 8 pages, will be presented in 52nd Conference on Decision and Contro
Robust Estimation of Optical Phase Varying as a Continuous Resonant Process
It is well-known that adaptive homodyne estimation of continuously varying
optical phase provides superior accuracy in the phase estimate as compared to
adaptive or non-adaptive static estimation. However, most phase estimation
schemes rely on precise knowledge of the underlying parameters of the system
under measurement, and performance deteriorates significantly with changes in
these parameters; hence it is desired to develop robust estimation techniques
immune to such uncertainties. In related works, we have already shown how
adaptive homodyne estimation can be made robust to uncertainty in an underlying
parameter of the phase varying as a simplistic Ornstein-Uhlenbeck stochastic
noise process. Here, we demonstrate robust phase estimation for a more
complicated resonant noise process using a guaranteed cost robust filter.Comment: 5 pages, 10 figures, Proceedings of the 2013 Multi-Conference on
Systems and Contro
Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression
In this paper, we revisit batch state estimation through the lens of Gaussian
process (GP) regression. We consider continuous-discrete estimation problems
wherein a trajectory is viewed as a one-dimensional GP, with time as the
independent variable. Our continuous-time prior can be defined by any
nonlinear, time-varying stochastic differential equation driven by white noise;
this allows the possibility of smoothing our trajectory estimates using a
variety of vehicle dynamics models (e.g., `constant-velocity'). We show that
this class of prior results in an inverse kernel matrix (i.e., covariance
matrix between all pairs of measurement times) that is exactly sparse
(block-tridiagonal) and that this can be exploited to carry out GP regression
(and interpolation) very efficiently. When the prior is based on a linear,
time-varying stochastic differential equation and the measurement model is also
linear, this GP approach is equivalent to classical, discrete-time smoothing
(at the measurement times); when a nonlinearity is present, we iterate over the
whole trajectory to maximize accuracy. We test the approach experimentally on a
simultaneous trajectory estimation and mapping problem using a mobile robot
dataset.Comment: Submitted to Autonomous Robots on 20 November 2014, manuscript #
AURO-D-14-00185, 16 pages, 7 figure
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
Smoothing and filtering with a class of outer measures
Filtering and smoothing with a generalised representation of uncertainty is
considered. Here, uncertainty is represented using a class of outer measures.
It is shown how this representation of uncertainty can be propagated using
outer-measure-type versions of Markov kernels and generalised Bayesian-like
update equations. This leads to a system of generalised smoothing and filtering
equations where integrals are replaced by supremums and probability density
functions are replaced by positive functions with supremum equal to one.
Interestingly, these equations retain most of the structure found in the
classical Bayesian filtering framework. It is additionally shown that the
Kalman filter recursion can be recovered from weaker assumptions on the
available information on the corresponding hidden Markov model
Quasi-continuous higher-order sliding mode controller designs for spacecraft attitude tracking manoeuvres
This paper studies high-order sliding mode control laws to deal with some spacecraft attitude tracking problems. Second and third order quasi-continuous sliding control are applied to quaternion-based spacecraft attitude tracking manoeuvres. A class of linear sliding manifolds is selected as a function of angular velocities and quaternion errors. The second method of Lyapunov theory is used to show that tracking is achieved globally. An example of multiaxial attitude tracking manoeuvres is presented and simulation results are included to verify and compare the usefulness of the various controllers
Distributed state estimation in sensor networks with randomly occurring nonlinearities subject to time delays
This is the post-print version of the Article. The official published version can be accessed from the links below - Copyright @ 2012 ACM.This article is concerned with a new distributed state estimation problem for a class of dynamical systems in sensor networks. The target plant is described by a set of differential equations disturbed by a Brownian motion and randomly occurring nonlinearities (RONs) subject to time delays. The RONs are investigated here to reflect network-induced randomly occurring regulation of the delayed states on the current ones. Through available measurement output transmitted from the sensors, a distributed state estimator is designed to estimate the states of the target system, where each sensor can communicate with the neighboring sensors according to the given topology by means of a directed graph. The state estimation is carried out in a distributed way and is therefore applicable to online application. By resorting to the Lyapunov functional combined with stochastic analysis techniques, several delay-dependent criteria are established that not only ensure the estimation error to be globally asymptotically stable in the mean square, but also guarantee the existence of the desired estimator gains that can then be explicitly expressed when certain matrix inequalities are solved. A numerical example is given to verify the designed distributed state estimators.This work was supported in part by the National Natural Science Foundation of China under Grants 61028008, 60804028 and 61174136, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK,
and the Alexander von Humboldt Foundation of Germany
Quasi-continuous higher-order sliding-mode controllers for spacecraft-attitude-tracking manoeuvres
This paper studies higher order sliding-modecontrol laws to deal with some spacecraft-attitude-tracking problems. Quasi-continuous second- and third-order sliding controllers and differentiators are applied to quaternion-based spacecraftattitude- tracking maneuvers. A class of linear sliding manifolds is selected as a function of angular velocities and quaternion errors. The second method of Lyapunov is used to show that tracking is achieved globally. An example of multiaxial attitude-tracking maneuvers is presented, and simulation results are included to verify and compare the practical usefulness of the various controllers
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