Model validation: a connection between robust control and identification
Roy S. Smith,John Doyle +1 more
TLDR
The model validation problem addressed is: given experimental data and a model with both additive noise and norm-bounded perturbations, is it possible that the model could produce the observed input-output data?Abstract:
The gap between the models used in control synthesis and those obtained from identification experiments is considered by investigating the connection between uncertain models and data. The model validation problem addressed is: given experimental data and a model with both additive noise and norm-bounded perturbations, is it possible that the model could produce the observed input-output data? This problem is studied for the standard H/sub infinity // mu framework models. A necessary condition for such a model to describe an experimental datum is obtained. For a large class of models in the robust control framework, this condition is computable as the solution of a quadratic optimization problem. >read more
Citations
More filters
Journal ArticleDOI
Model Error Modeling and Control Design
TL;DR: The model error concepts and model error modeling for control design are discussed, of special interest is how to make use of periodic inputs, and how to deal with non-linear error models.
Proceedings ArticleDOI
A set-based framework for coherent model invalidation and parameter estimation of discrete time nonlinear systems
TL;DR: This work introduces a unified framework for model invalidation and parameter estimation for nonlinear systems by exploiting the polynomial structure of the considered model class to relaxed into a convex semi-definite one, for which infeasibility can be efficiently checked.
Journal ArticleDOI
Frequency Domain Tests for Validation of Linear Fractional Uncertain Models
TL;DR: In this paper, a frequency domain approach is adopted to tackle the problem of validating uncertainty models described by linear fractional transforms, and the validation problem reduces to one of Nevanlinna-Pick boundary interpolation, and can be solved by solving independently a sequence of convex programs defined with respect to each sampling frequency.
Journal ArticleDOI
Brief Off-line robust fault diagnosis using the generalized structured singular value
TL;DR: A novel step-by-step methodology for off-line fault identification and symptom-aided diagnostic is developed that uses the generalized structured singular value and is based on frequency-domain model invalidation tools.
References
More filters
Analysis of feedback systems with structured uncertainties
TL;DR: In this article, a general approach for analysing linear systems with structured uncertainty based on a new generalised spectral theory for matrices is introduced, which naturally extend techniques based on singular values and eliminate their most serious difficulties.
Proceedings ArticleDOI
Structured uncertainty in control system design
TL;DR: This paper reviews control system analysis and synthesis techniques for robust performance with structured uncertainty in the form of multiple unstructured perturbations and parameter variations in the case where parameter variations are known to be real.
Journal ArticleDOI
Control oriented system identification: a worst-case/deterministic approach in H/sub infinity /
TL;DR: The authors formulate and solve two related control-oriented system identification problems for stable linear shift-invariant distributed parameter plants, each involving identification of a point sample of the plant frequency response from a noisy, finite, output time series obtained in response to an applied sinusoidal input.