9 research outputs found
Supervisory observer for parameter and state estimation of nonlinear systems using the DIRECT algorithm
A supervisory observer is a multiple-model architecture, which estimates the
parameters and the states of nonlinear systems. It consists of a bank of state
observers, where each observer is designed for some nominal parameter values
sampled in a known parameter set. A selection criterion is used to select a
single observer at each time instant, which provides its state estimate and
parameter value. The sampling of the parameter set plays a crucial role in this
approach. Existing works require a sufficiently large number of parameter
samples, but no explicit lower bound on this number is provided. The aim of
this work is to overcome this limitation by sampling the parameter set
automatically using an iterative global optimisation method, called DIviding
RECTangles (DIRECT). Using this sampling policy, we start with 1 + 2np
parameter samples where np is the dimension of the parameter set. Then, the
algorithm iteratively adds samples to improve its estimation accuracy.
Convergence guarantees are provided under the same assumptions as in previous
works, which include a persistency of excitation condition. The efficacy of the
supervisory observer with the DIRECT sampling policy is illustrated on a model
of neural populations
On the Design and Analysis of Multiple View Descriptors
We propose an extension of popular descriptors based on gradient orientation
histograms (HOG, computed in a single image) to multiple views. It hinges on
interpreting HOG as a conditional density in the space of sampled images, where
the effects of nuisance factors such as viewpoint and illumination are
marginalized. However, such marginalization is performed with respect to a very
coarse approximation of the underlying distribution. Our extension leverages on
the fact that multiple views of the same scene allow separating intrinsic from
nuisance variability, and thus afford better marginalization of the latter. The
result is a descriptor that has the same complexity of single-view HOG, and can
be compared in the same manner, but exploits multiple views to better trade off
insensitivity to nuisance variability with specificity to intrinsic
variability. We also introduce a novel multi-view wide-baseline matching
dataset, consisting of a mixture of real and synthetic objects with ground
truthed camera motion and dense three-dimensional geometry
Accelerated convergence of neural network system identification algorithms via principal component analysis
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76898/1/AIAA-1998-4440-926.pd
An information filter approach to rapid system identification - Convergence speed and noise sensitivity
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76081/1/AIAA-1998-4510-927.pd
Variance computation for system matrices and transfer function from input/output subspace system identification
International audienceThe transfer function of a linear system is defined in terms of the quadruplet of matrices (A, B, C, D) that can be identified from input and output measurements. Similarly these matrices determine the state space evolution for the considered dynamical system. Estimation of the quadruplet has been well studied in the literature from both theoretical and practical points of view. Nonetheless, the uncertainty quantification of their estimation errors has been mainly discussed from a theoretical viewpoint. For several output-only and input/output subspace methods, the variance of the (A, C) matrices can be effectively obtained with recently developed first-order perturbation-based schemes. This paper addresses the estimation of the (B, D) matrices, and the remaining problem of the effective variance computation of their estimates and the resulting transfer function. The proposed schemes are validated on a simulation of a mechanical system
Adaptive Control of Robotic Manipulators using Deep Neural Networks
In this paper, we present a lifelong deep learning-based control of robotic manipulators with nonstandard adaptive laws using singular value decomposition (SVD) based direct tracking error driven (DTED) approach. Moreover, we incorporate concurrent learning (CL) to relax persistency of excitation condition and elastic weight consolidation (EWC) for lifelong learning on different tasks in the adaptive laws. Simulation results confirm theoretical conclusions
Uncertainty quantification of input matrices and transfer function in input/output subspace system identification
International audienceThe transfer function of a linear mechanical system can be defined in terms of the quadruplet of statespace matrices (A, B, C, D) that can be identified from input and output measurements with subspace-based system identification methods. The estimation of the quadruplet has been well studied in the literature from both theoretical and practical viewpoints. Nonetheless, a practical algorithm for uncertainty quantification of its estimation errors and the uncertainty of the resultant parametric transfer function is missing in the context of subspace identification. For several output-only and input/output subspace methods, the covariance related to the matrices (A, C) and to the resulting modal parameters can be effectively obtained with recently developed first-order perturbation-based schemes, while the corresponding uncertainty quantification for the input-related matrices (B, D) is missing. In this paper, explicit expressions for the covariance related to matrices (B, D) are developed, and applied to the covariance estimation of the resulting transfer function. The proposed schemes are validated on simulated data of a mechanical system and are applied to laboratory measurements of a plate
Estimation and tracking of rapidly time-varying broadband acoustic communication channels
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2006This thesis develops methods for estimating wideband shallow-water acoustic communication
channels. The very shallow water wideband channel has three distinct features: large dimension caused by extensive delay spread; limited number of degrees of freedom (DOF) due to resolvable paths and inter-path correlations; and rapid fluctuations induced by scattering from the moving sea surface. Traditional LS estimation techniques often fail to reconcile the rapid fluctuations with the large
dimensionality. Subspace based approaches with DOF reduction are confronted with unstable subspace structure subject to significant changes over a short period of time. Based on state-space channel modeling, the first part of this thesis develops algorithms that jointly estimate the channel as well as its dynamics. Algorithms based on the Extended Kalman Filter (EKF) and the Expectation Maximization (EM) approach respectively are developed. Analysis shows conceptual parallels, including
an identical second-order innovation form shared by the EKF modification and the suboptimal EM, and the shared issue of parameter identifiability due to channel structure, reflected as parameter unobservability in EKF and insufficient excitation in EM. Modifications of both algorithms, including a two-model based EKF and a subspace EM algorithm which selectively track dominant taps and reduce prediction error, are proposed to overcome the identifiability issue. The second part of the thesis
develops algorithms that explicitly find the sparse estimate of the delay-Doppler spread function.
The study contributes to a better understanding of the channel physical constraints on algorithm design and potential performance improvement. It may also be generalized to other applications where dimensionality and variability collide.Financial support for this thesis research was provided by the Office of Naval
Research and the WHOI Academic Program Office