Szczegóły publikacji
Opis bibliograficzny
Neural net model predictive controller for adaptive active vibration suppression of an unknown system / Mateusz HEESCH, Ziemowit DWORAKOWSKI // W: Artificial Intelligence and Soft Computing : 18th international conference, ICAISC 2019 : Zakopane, June 16–20, 2019 : proceedings, Pt. 1 / eds. Leszek Rutkowski, [et al.]. — Cham : Springer Nature Switzerland AG, cop. 2019. — (Lecture Notes in Computer Science ; ISSN 0302-9743. Lecture Notes in Artificial Intelligence ; LNAI 11508). — ISBN: 978-3-030-20911-7; e-ISBN: 978-3-030-20912-4. — S. 102-112. — Bibliogr. s. 111-112, Abstr.
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 123351 |
|---|---|
| Data dodania do BaDAP | 2019-09-23 |
| Tekst źródłowy | URL |
| DOI | 10.1007/978-3-030-20912-4_10 |
| Rok publikacji | 2019 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Artificial Intelligence and Soft Computing 2019 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Active vibration suppression is a well explored area when it comes to simple problems, however as the problem complexity grows to a time variant system, the amount of researched solutions drops by a large margin, which is further increased with the added requirement of very limited knowledge about the controlled system. These conditions make the problem significantly more complicated, often rendering classic approaches suboptimal or unusable, requiring a more intelligent approach - such as utilizing soft computing. This work proposes a Artificial Neural Network (ANN) Model Predictive Control (MPC) scheme, inspired by horizon techniques which are used for MPC. The proposed approach aims to solve the problem of active vibration control of an unknown and largely unobservable time variant system, while attempting to keep the controller fast by introducing several methods of reducing the amount of calculations inside the control loop - which with proper tuning have no negative impact on the controller’s performance. The proposed approach outperforms the multi-input Proportional-Derivative (PD) controller preoptimized using a genetic algorithm. © 2019, Springer Nature Switzerland AG.
