Robust linear matrix inequality‐based model predictive control with recursive estimation of the uncertainty polytope. Issue 6 (1st April 2013)

• Record Type: Journal Article
• Authors: Cavalca, Mariana Santos Matos; Galvão, Roberto Kawakami Harrop; Yoneyama, Takashi
• Is Part Of: IET control theory & applications. Volume 7:Issue 6(2013)
• Page Start: 901
• Page End: 909
• Publication Date: 2013-04-01
• Subjects: asymptotic stability -- linear matrix inequalities -- position control -- predictive control -- recursive estimation -- robust control -- sampling methods -- sensors -- uncertain systems; robust linear matrix inequality‐based model predictive control -- recursive estimation -- uncertainty polytope -- RMPC framework -- bounded error method -- sensor measurements -- sampling period -- asymptotic stability properties -- recursive feasibility properties -- RMPC formulation -- angular positioning system -- performance improvement -- cost function values; asymptotic stability -- linear matrix inequalities -- position control -- predictive control -- recursive estimation -- robust control -- sampling methods -- sensors -- uncertain systems; robust linear matrix inequality‐based model predictive control -- recursive estimation -- uncertainty polytope -- RMPC framework -- bounded error method -- sensor measurements -- sampling period -- asymptotic stability properties -- recursive feasibility properties -- RMPC formulation -- angular positioning system -- performance improvement -- cost function values; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2012.0586
• Languages: English
• ISSNs: 1751-8644

Abstract

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.