Robust Quasi-LPV Control Based on Neural State Space Models

Jan Dimon Bendtsen, Klaus Trangbæk

Publikation: Bidrag til tidsskriftTidsskriftartikelFormidling

19 Citationer (Scopus)

Resumé

In this paper we derive a synthesis result for robust LPV output feedback controllers for nonlinear systems modelled by neural state space models. This result is achieved by writing the neural state space model on a linear fractional transformation form in a non-conservative way, separating the system description into a linear part and a nonlinear part. Linear parameter-varying control synthesis methods are then applied to design a nonlinear control law for this system. Since the model is assumed to have been identified from input-output measurement data only, it must be expected that there is some uncertainty on the identified nonlinearities. The control law is therefore made robust to noise perturbations. After formulating the controller synthesis as a set of LMIs with added constraints, some implementation issues are addressed and a simulation example is presented.
OriginalsprogDansk
TidsskriftIEEE Transactions on Neural Networks
Sider (fra-til)355-368
ISSN1045-9227
StatusUdgivet - 2002

Citer dette

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Robust Quasi-LPV Control Based on Neural State Space Models. / Bendtsen, Jan Dimon; Trangbæk, Klaus.

I: IEEE Transactions on Neural Networks, 2002, s. 355-368.

Publikation: Bidrag til tidsskriftTidsskriftartikelFormidling

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