Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Carlos Ariño"'
Publikováno v:
Revista Iberoamericana de Automática e Informática Industrial RIAI, Vol 8, Iss 3, Pp 167-181 (2011)
Resumen: Este trabajo propone una solución explícita para el control predictivo de sistemas lineales sujetos a restricciones poliédricas no convexas, modeladas como la unión de un número finito de poliedros. El algoritmo se basa en el cálculo d
Externí odkaz:
https://doaj.org/article/f420cb2073414c03adb22e4d84a40a5d
Publikováno v:
Revista Iberoamericana de Automática e Informática Industrial RIAI, Vol 4, Iss 2, Pp 98-105 (2007)
Resumen: En el diseño de controladores para sistemas borrosos Takagi-Sugeno (TS) se asume la verificatión de ciertas condiciones de sector; sin embargo, los cambios en la referencia pueden alterar la validez de esas condiciones. El objetivo de este
Externí odkaz:
https://doaj.org/article/22104febe08c43d9b9b6dc0ba7a3ab4a
Publikováno v:
International Journal of Robust and Nonlinear Control. 31:8124-8146
A nonlinear system with sector-bounded nonlinearities may be expressed as a quasiLPV system (convex combination of linear models), being this a well-known fact. The convex difference inclusion (CDI) modelling framework proposed by M. Fiacchini and co
Publikováno v:
IFAC-PapersOnLine. 52:70-75
A nonlinear system with sector-bounded nonlinearities can be expressed as a quasi-LPV system (convex combination of linear models, with state-dependent interpolation coefficients). The quasi-LPV class of models is generalised to the so-called convex
Autor:
Antonio Sala, Carlos Ariño
Publikováno v:
Repositori Universitat Jaume I
Universitat Jaume I
Universitat Jaume I
Comunicació presentada a ICONS 2019 5th IFAC Intelligent Control and Automation Sciences (August 21-23, 2019, Queen’s University Belfast, Northern Ireland) In this paper, an approximation of the smallest set (in a given norm) to which the state of
Autor:
Carlos Ariño, P. Balaguer
Publikováno v:
Repositori Universitat Jaume I
Universitat Jaume I
Universitat Jaume I
The optimal sampling problem is the selection of the optimal sampling instants together with the optimal control actions such that a given cost function is minimized. In this article, we solve the optimal sampling problem for the free final time line
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0eaf9b54aed7f4b09526e5d4c6ae39e
http://hdl.handle.net/10234/196820
http://hdl.handle.net/10234/196820
Publikováno v:
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname
Revista Iberoamericana de Automática e Informática Industrial RIAI, Vol 12, Iss 4, Pp 457-466 (2015)
Repositori Universitat Jaume I
Universitat Jaume I
instname
Revista Iberoamericana de Automática e Informática Industrial RIAI, Vol 12, Iss 4, Pp 457-466 (2015)
Repositori Universitat Jaume I
Universitat Jaume I
[ES] El presente trabajo analiza el comportamiento de sistemas borrosos Takagi-Sugeno ante perturbaciones persistentes (caracterizadas bien por cotas conocidas de amplitud o de potencia en media cuadrática). El análisis se centra en validar que, an
Publikováno v:
International Journal of Robust and Nonlinear Control. 26:2075-2089
Summary Robust λ-contractive sets have been proposed in previous literature for uncertain polytopic linear systems. It is well known that, if initial state is inside such sets, it is guaranteed to converge to the origin. This work presents the gener
Publikováno v:
IFAC-PapersOnLine. 48:230-235
This paper proposes a model predictive control for Markov-jump or switched linear systems (switching between a finite set of modes) when simultaneously considering prediction horizons (and realisations of the mode variable) of different length, in or
This paper discusses predictive control for constrained discrete-time Markov-jump linear systems (MJLS) which jump between a finite set of modes according to a Markov probabilistic transition/observation model, minimising an average cost. Due to the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79b0cfabca953a77ff43065010044947