Robust linear matrix inequality-based model predictive control with recursive estimation of the uncertainty polytope

Author(s)

Matos Cavalca, Mariana Santos ^[1] ; Harrop Galvao, Roberto Kawakami ^[2] ; Yoneyama, Takashi ^[2]

Affiliation

^[1] Univ Estado Santa Catarina, Ctr Ciencias Tecnol, Dept Engn Eletr, BR-89219710 Joinville, SC - Brazil

^[2] Inst Tecnol Aeronaut, Div Engn Eletron, BR-12228900 Sao Jose Dos Campos, SP - Brazil

• Document type: Journal article
• Source: IET Control Theory and Applications; v. 7, n. 6, p. 901-909, APR 2013.
• Web of Science Citations: 4

Abstract

The present work is concerned with the recursive estimation of the uncertainty polytope in a robust model predictive control (RMPC) framework. For this purpose, the unknown but bounded error method is employed to update the uncertainty polytope on the basis of sensor measurements at each sampling period. The recursive feasibility and asymptotic stability properties of the proposed approach are demonstrated as an extension of previous results concerning the RMPC formulation. For illustration, a simulated example involving an angular positioning system is presented. The results show that the proposed scheme provides a performance improvement, as indicated by the resulting cost function values. (AU)

• FAPESP's process: 06/58850-6 - Diagnosis, prognosis and fault accommodation for dynamical systems
• Grantee: Takashi Yoneyama
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 11/17610-0 - Monitoring and control of dynamic systems subject to faults
• Grantee: Roberto Kawakami Harrop Galvão
• Support Opportunities: Research Projects - Thematic Grants