### Resumé

Originalsprog | Dansk |
---|---|

Tidsskrift | IEEE Transactions on Neural Networks |

Sider (fra-til) | 355-368 |

ISSN | 1045-9227 |

Status | Udgivet - 2002 |

### Citer dette

*IEEE Transactions on Neural Networks*, 355-368.

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*IEEE Transactions on Neural Networks*, s. 355-368.

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

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Formidling

TY - JOUR

T1 - Robust Quasi-LPV Control Based on Neural State Space Models

AU - Bendtsen, Jan Dimon

AU - Trangbæk, Klaus

PY - 2002

Y1 - 2002

N2 - 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.

AB - 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.

M3 - Tidsskriftartikel

SP - 355

EP - 368

JO - I E E E Transactions on Neural Networks and Learning Systems

JF - I E E E Transactions on Neural Networks and Learning Systems

SN - 2162-237X

ER -