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
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Proceedings ArticleDOI
Safe Inputs Approximation for Black-Box Systems
TL;DR: This paper proposes a method to under-approximate the set of safe inputs that lead the black-box system to respect a given safety specification, which falls within the framework of probably approximately correct (PAC) learning.
Proceedings ArticleDOI
Probabilistic model validation problems with H/sub /spl infin// type uncertainties
Wenguo Liu,Jie Chen +1 more
TL;DR: In this article, a mixed deterministic/probabilistic model validation problem is investigated, which consists in an additive uncertain model with uncertainty characterized by the H/sub /spl infin// norm, and time-domain experimental data corrupted by a Gaussian noise sequence.
Proceedings ArticleDOI
Experimental validation of a truck roll model using asynchronous measurements with low signal-to-noise ratios
TL;DR: In this paper, a 4-DOF roll model was used to evaluate the front dynamics of a tractor semi-trailer system (heavy-duty truck), which is confronted with a large set of measurement data obtained from various experimental driving tests.
Dissertation
Identification and Self-Tuning Control of Dynamic Systems
TL;DR: The types and the basic formulation of self-tuners are presented and the algorithm for Minimum Variance Control in general is explained and some identification techniques that are most popular for self- Tuners are included.
Book ChapterDOI
Control of Mixing and Reactive Flow Processes
TL;DR: In this paper, the potential benefits and technical challenges for mixing and combustion control in fundamental as well as practical systems and to identify promising research directions that could help meet these challenges are outlined.
References
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