Szczegóły publikacji
Opis bibliograficzny
Fuzzy logic and neural network approach to the indirect adaptive pole placement crane control system / Jarosław SMOCZEK, Janusz SZPYTKO // Journal of KONES ; ISSN 1231-4005. — 2010 — vol. 17 no. 2, s. 435–444. — Bibliogr. s. 443–444, Abstr.
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 55956 |
|---|---|
| Data dodania do BaDAP | 2011-01-11 |
| Tekst źródłowy | URL |
| Rok publikacji | 2010 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of KONES |
Abstract
The problem under consideration in the paper of automation transportation operation realized by material handling devices is focused on time and accuracy of an overhead travelling crane’s shifting process. The presented anti-sway crane control system was solved in the paper using combination of an indirect adaptive pole placement (IAPP) control method, fuzzy logic and artificial neural network. The presented approach to crane control is based on assuming structure of crane dynamic linear model with varying parameters, and linear closed-loop discrete control system consisting of proportional- derivative controllers with gains adjusted to changes of model’s parameters using pole placement method (PPM). The parameters of crane dynamic model are estimated on-line using recursive least squares (RLS) algorithm. The estimation process is speeded up by neuro-fuzzy estimator, created using TakagiSugeno-Kang (TSK) fuzzy inference system, which determines the initial parameters of crane model based on scheduling variables, rope length and mass of a load changing in stochastic way. The neuro-fuzzy estimator is created in off-line process of neural network learning using least mean squares (LMS) method, based on a set of parametric output error models of crane dynamic identified for fixed values of rope length and mass of a load. The TSK estimator is next on-line improved by RLS algorithm.
