Szczegóły publikacji
Opis bibliograficzny
Nonlinear model predictive control with evolutionary data-driven prediction model and particle swarm optimization optimizer for an overhead crane / Tom KUSZNIR, Jarosław SMOCZEK // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417. — 2024 — vol. 14 iss. 12 art. no. 5112, s. 1–18. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 16–18, Abstr. — Publikacja dostępna online od: 2024-06-12
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 153767 |
|---|---|
| Data dodania do BaDAP | 2024-06-17 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app14125112 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regression method. Grammar-guided genetic programming combined with regularized least squares was applied to identify a nonlinear autoregressive model with an exogenous input (NARX) prediction model of the crane dynamics from input–output data. The resulting prediction model was implemented in the NMPC scheme, using a particle swarm optimization (PSO) algorithm as a solver to find an optimal sequence of the control actions satisfying multi-objective performance requirements and input constraints. The feasibility and performance of the controller were experimentally verified using a laboratory crane actuated by AC motors and compared with a discrete-time feedback controller developed using the pole placement technique. A series of experiments proved the effectiveness of the controller in terms of robustness against operating condition variation and external disturbances.
