Pregled bibliografske jedinice broj: 653224
Self-optimizing Robust Nonlinear Model Predictive Control
Self-optimizing Robust Nonlinear Model Predictive Control // Assessment and Future Directions of Nonlinear Model Predictive Control
Italija: Springer, 2009. str. 27-40 (pozvano predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 653224 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Self-optimizing Robust Nonlinear Model Predictive Control
Autori
Lazar, M. ; Heemels, W.P.M.H. ; Jokić, Andrej
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Assessment and Future Directions of Nonlinear Model Predictive Control
/ - : Springer, 2009, 27-40
Skup
Assessment and Future Directions of Nonlinear Model Predictive Control
Mjesto i datum
Italija, 2009
Vrsta sudjelovanja
Pozvano predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
nonlinear systems; robust model predictive control (MPC); input-tostate stability (ISS); decentralized control.
Sažetak
This paper presents a novel method for designing robust MPC schemes that are self-optimizing in terms of disturbance attenuation. The method employs convex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant - a unique feature in MPC. Moreover, the proposed MPC algorithm is computationally efficient for nonlinear systems that are affine in the control input and it allows for a decentralized implementation.
Izvorni jezik
Engleski
Znanstvena područja
Temeljne tehničke znanosti
