5,070 research outputs found
A survey on fractional order control techniques for unmanned aerial and ground vehicles
In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade
A Nonparametric Adaptive Nonlinear Statistical Filter
We use statistical learning methods to construct an adaptive state estimator
for nonlinear stochastic systems. Optimal state estimation, in the form of a
Kalman filter, requires knowledge of the system's process and measurement
uncertainty. We propose that these uncertainties can be estimated from
(conditioned on) past observed data, and without making any assumptions of the
system's prior distribution. The system's prior distribution at each time step
is constructed from an ensemble of least-squares estimates on sub-sampled sets
of the data via jackknife sampling. As new data is acquired, the state
estimates, process uncertainty, and measurement uncertainty are updated
accordingly, as described in this manuscript.Comment: Accepted at the 2014 IEEE Conference on Decision and Contro
Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation
In this paper, we present the optimization formulation of the Kalman
filtering and smoothing problems, and use this perspective to develop a variety
of extensions and applications. We first formulate classic Kalman smoothing as
a least squares problem, highlight special structure, and show that the classic
filtering and smoothing algorithms are equivalent to a particular algorithm for
solving this problem. Once this equivalence is established, we present
extensions of Kalman smoothing to systems with nonlinear process and
measurement models, systems with linear and nonlinear inequality constraints,
systems with outliers in the measurements or sudden changes in the state, and
systems where the sparsity of the state sequence must be accounted for. All
extensions preserve the computational efficiency of the classic algorithms, and
most of the extensions are illustrated with numerical examples, which are part
of an open source Kalman smoothing Matlab/Octave package.Comment: 46 pages, 11 figure
Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime
There are different strategies for training neural networks (NNs) as
subgrid-scale parameterizations. Here, we use a 1D model of the quasi-biennial
oscillation (QBO) and gravity wave (GW) parameterizations as testbeds. A
12-layer convolutional NN that predicts GW forcings for given wind profiles,
when trained offline in a big-data regime (100-years), produces realistic QBOs
once coupled to the 1D model. In contrast, offline training of this NN in a
small-data regime (18-months) yields unrealistic QBOs. However, online
re-training of just two layers of this NN using ensemble Kalman inversion and
only time-averaged QBO statistics leads to parameterizations that yield
realistic QBOs. Fourier analysis of these three NNs' kernels suggests why/how
re-training works and reveals that these NNs primarily learn low-pass,
high-pass, and a combination of band-pass filters, consistent with the
importance of both local and non-local dynamics in GW propagation/dissipation.
These findings/strategies apply to data-driven parameterizations of other
climate processes generally
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