Szczegóły publikacji
Opis bibliograficzny
Robust model predictive sliding mode control of linear parameter varying systems / Mohammadsadegh Baneshi, Mohsen Farbood, Anthony Ja Rossiter, Saleh Mobayen, Paweł SKRUCH, Ruggero Carli // IEEE Access [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2169-3536. — 2025 — vol. 13, s. 64291–64304. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 64303–64304, Abstr.
Autorzy (6)
- Baneshi Mohammadsadegh
- Farbood Mohsen
- Rossiter Anthony Ja
- Mobayen Saleh
- AGHSkruch Paweł
- Carli Ruggero
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 159657 |
|---|---|
| Data dodania do BaDAP | 2025-06-04 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ACCESS.2025.3558973 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | IEEE Access |
Abstract
The stability and performance of nonlinear systems under uncertainty are guaranteed by this paper's robust model predictive control (MPC) approach, which combines sliding mode control (SMC) with a polytopic linear parameter varying (PLPV) framework. The approach begins with the development of a PLPV integral SMC aimed at effectively managing model uncertainties and external disturbances. Subsequently, a PLPV MPC is formulated using a Lyapunov function along with an ellipsoidal terminal constraint to enhance system reliability. This means, after ensuring robust performance in the face of uncertainties, the MPC is designed, incorporating the integral SMC. This implies that the MPC is designed online to optimize system performance and account for system constraints, with robustness provided offline by the SMC. The suggested predictive controller is based on a nominal system and ensures robustness in the offline process with SMC. Compared to existing robust MPC approaches, such as tube-based MPC and max-min MPC, it demonstrates reduced computational burden and conservatism. Finally, the effectiveness of the proposed framework is validated through numerical examples and experimental tests, in comparison to existing robust MPC and traditional SMC.
