Model Predictive Control of Stochastic Linear Systems with Probability Constraints

Autor(es)

Caruntu, C. F. ^[1] ; Velandia-Cardenas, C. C. ^[2] ; Liu, X. ^[3] ; Vargas, A. N. ^[4]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL; v. 13, n. 6, p. 927-937, DEC 2018.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 03/06736-7 - Controle e filtragem de sistemas estocásticos markovianos com saltos nos parâmetros
• Beneficiário: João Bosco Ribeiro do Val
• Modalidade de apoio: Auxílio à Pesquisa - Temático