Nonparametric model error bounds for control design in the presence of nonlinear distortions

2003 ◽  
Author(s):  
J. Schoukens ◽  
R. Pintelon ◽  
T. Dobrowiecki
Keyword(s):  
Error Bounds ◽  
Control Design ◽  
Model Error ◽  
Nonparametric Model ◽  
Nonlinear Distortions

scholarly journals Parametric and nonparametric models of the impedance matrix of a random medium

2008 ◽  
pp. 881-892
Author(s):  
Régis Cottereau ◽  
Didier Clouteau ◽  
Christian Soize
Keyword(s):  
Deterministic Model ◽  
Random Medium ◽  
Model Error ◽  
The Other ◽  
Nonparametric Model ◽  
Impedance Matrix ◽  
Model Errors ◽  
Parametric Approach ◽  
Nonparametric Models ◽  
Very High

Two approaches are presented for the modeling of the impedance matrix of a random medium: one parametric and the other nonparametric. The former allows to take into account the data uncertainties while introducing a model error, that yields, in some cases, very high levels. The latter is based on a much simpler, deterministic, model, for which both data uncertainties and model errors are accounted for. When the model error is negligible, the parametric approach can be used for the identification of the parameters of the nonparametric model of the impedance matrix.

Model error concepts in control design

1989 ◽  
Vol 49 (5) ◽  
pp. 1725-1753 ◽  
Author(s):  
R. E. Skelton
Keyword(s):  
Control Design ◽  
Model Error

scholarly journals Uncertainty propagation in model extraction by system identification and its implication for control design

2016 ◽  
Vol 791 ◽  
pp. 214-236 ◽  
Author(s):  
Nicolas Dovetta ◽  
Peter J. Schmid ◽  
Denis Sipp
Keyword(s):  
Monte Carlo ◽  
System Identification ◽  
Error Bounds ◽  
Control Design ◽  
Design System ◽  
Input Output ◽  
Input Space ◽  
System Parameters ◽  
System Representation ◽  
Low Dimensional

In data-based control design, system-identification techniques are used to extract low-dimensional representations of the input–output map between actuators and sensors from observed data signals. Under realistic conditions, noise in the signals is present and is expected to influence the identified system representation. For the subsequent design of the controller, it is important to gauge the sensitivity of the system representation to noise in the observed data; this information will impact the robustness of the controller and influence the stability margins for a closed-loop configuration. Commonly, full Monte Carlo analysis has been used to quantify the effect of data noise on the system identification and control design, but in fluid systems, this approach is often prohibitively expensive, due to the high dimensionality of the data input space, for both numerical simulations and physical experiments. Instead, we present a framework for the estimation of statistical properties of identified system representations given an uncertainty in the processed data. Our approach consists of a perturbative method, relating noise in the data to identified system parameters, which is followed by a Monte Carlo technique to propagate uncertainties in the system parameters to error bounds in Nyquist and Bode plots. This hybrid approach combines accuracy, by treating the system-identification part perturbatively, and computational efficiency, by applying Monte Carlo techniques to the low-dimensional input space of the control design and performance/stability evaluation part. This combination makes the proposed technique affordable and efficient even for large-scale flow-control problems. The ARMarkov/LS identification procedure has been chosen as a representative system-identification technique to illustrate this framework and to obtain error bounds on the identified system parameters based on the signal-to-noise ratio of the input–output data sequence. The procedure is illustrated on the control design for flow over an idealized aerofoil with a trailing-edge splitter plate.

Sign in / Sign up

Export Citation Format

Share Document