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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Model Predictive Control of Stochastic Linear Systems with Probability Constraints

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Author(s):
Caruntu, C. F. [1] ; Velandia-Cardenas, C. C. [2] ; Liu, X. [3] ; Vargas, A. N. [4]
Total Authors: 4
Affiliation:
[1] Gheorghe Asachi Tech Univ Iasi, Dept Automat Control & Appl Informat, Str Prof D Mangeron 27, Iasi - Romania
[2] Univ Santo Tomas, Fac Elect Engn, Res Grp MEM, Cra 9 51-11, Bogota - Colombia
[3] Xian Univ Technol, Sch Automat & Informat Engn, Dept Elect Engn, Xian 710048, Shaanxi - Peoples R China
[4] Univ Tecnol Fed Parana, UTFPR, Av Alberto Carazzai 1640, BR-86300000 Cornelio Procopio, PR - Brazil
Total Affiliations: 4
Document type: Journal article
Source: INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL; v. 13, n. 6, p. 927-937, DEC 2018.
Web of Science Citations: 0
Abstract

This paper presents a strategy for computing model predictive control of linear Gaussian noise systems with probability constraints. As usual, constraints are taken on the system state and control input. The novelty relies on setting bounds on the underlying cumulative probability distribution, and showing that the model predictive control can be computed in an efficient manner through these novel boundsan application confirms this assertion. Indeed real-time experiments were carried out to control a direct current (DC) motor. The corresponding data show the effectiveness and usefulness of the approach. (AU)

FAPESP's process: 03/06736-7 - Control and filtering of Markovian jumping parameters stochastic systems
Grantee:João Bosco Ribeiro do Val
Support type: Research Projects - Thematic Grants