39,138 research outputs found
On the interval estimation for nonlinear singular system
International audience— This paper investigates the interval observer design problem for a class of nonlinear singular systems with uncertainties in the state and in the output. Although the asymptotic estimation of the state for this class of systems may be not possible, it has been shown that, under suitable assumptions, an interval observer can be designed to provide the upper and the lower estimations of the real state. Moreover , this technique can be applied to unobservable nonlinear singular systems to obtain the interval estimation. The proposed result can be easily extended to deal with the nonlinear singular systems with parameter uncertainty as well
Efficiency and Sensitivity Analysis of Observation Networks for Atmospheric Inverse Modelling with Emissions
The controllability of advection-diffusion systems, subject to uncertain
initial values and emission rates, is estimated, given sparse and error
affected observations of prognostic state variables. In predictive geophysical
model systems, like atmospheric chemistry simulations, different parameter
families influence the temporal evolution of the system.This renders
initial-value-only optimisation by traditional data assimilation methods as
insufficient. In this paper, a quantitative assessment method on validation of
measurement configurations to optimize initial values and emission rates, and
how to balance them, is introduced. In this theoretical approach, Kalman filter
and smoother and their ensemble based versions are combined with a singular
value decomposition, to evaluate the potential improvement associated with
specific observational network configurations. Further, with the same singular
vector analysis for the efficiency of observations, their sensitivity to model
control can be identified by determining the direction and strength of maximum
perturbation in a finite-time interval.Comment: 30 pages, 10 figures, 5 table
Parameter Estimation for the Stochastically Perturbed Navier-Stokes Equations
We consider a parameter estimation problem to determine the viscosity
of a stochastically perturbed 2D Navier-Stokes system. We derive several
different classes of estimators based on the first Fourier modes of a
single sample path observed on a finite time interval. We study the consistency
and asymptotic normality of these estimators. Our analysis treats strong,
pathwise solutions for both the periodic and bounded domain cases in the
presence of an additive white (in time) noise.Comment: to appear in SP
Composite Learning Control With Application to Inverted Pendulums
Composite adaptive control (CAC) that integrates direct and indirect adaptive
control techniques can achieve smaller tracking errors and faster parameter
convergence compared with direct and indirect adaptive control techniques.
However, the condition of persistent excitation (PE) still has to be satisfied
to guarantee parameter convergence in CAC. This paper proposes a novel model
reference composite learning control (MRCLC) strategy for a class of affine
nonlinear systems with parametric uncertainties to guarantee parameter
convergence without the PE condition. In the composite learning, an integral
during a moving-time window is utilized to construct a prediction error, a
linear filter is applied to alleviate the derivation of plant states, and both
the tracking error and the prediction error are applied to update parametric
estimates. It is proven that the closed-loop system achieves global
exponential-like stability under interval excitation rather than PE of
regression functions. The effectiveness of the proposed MRCLC has been verified
by the application to an inverted pendulum control problem.Comment: 5 pages, 6 figures, conference submissio
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