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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.)

Algorithm 998: The Robust LMI Parser-A Toolbox to Construct LMI Conditions for Uncertain Systems

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Author(s):
Agulhari, Cristiano M. [1] ; Felipe, Alexandre [2, 3] ; Oliveira, Ricardo C. L. F. [2] ; Peres, Pedro L. D. [2]
Total Authors: 4
Affiliation:
[1] Univ Tecnol Fed Parana, Elect Engn Dept, UTFPR, Campus Cornelio Procopio, BR-86300000 Cornelio Procopio, PR - Brazil
[2] Univ Estadual Campinas, Sch Elect & Comp Engn, UNICAMP, BR-13083852 Campinas, SP - Brazil
[3] CPqD, Campinas, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE; v. 45, n. 3 AUG 2019.
Web of Science Citations: 2
Abstract

The ROLMIP (Robust LMI Parser) is a toolbox specialized in control theory for uncertain linear systems, built to work under MATLAB jointly with YALMIP, to ease the programming of sufficient Linear Matrix Inequality (LMI) conditions that, if feasible, assure the validity of parameter-dependent LMIs in the entire set of uncertainty considered. This article presents the new version of the ROLMIP toolbox, which was completely remodeled to provide a high-level user-friendly interface to cope with distinct uncertain domains (hypercube and multi-simplex) and to treat time-varying parameters in discrete- and continuous-time. By means of simple commands, the user is able to define polynomial matrices as well as to describe the desired parameter-dependent LMIs in an easy way, considerably reducing the programming time to end up with implementable LMI conditions. Therefore, ROLMIP helps the popularization of the state-of-the-art robust control methods for uncertain systems based on LMIs among graduate students, researchers, and engineers in control systems. (AU)

FAPESP's process: 17/18785-5 - Parameter-Dependent Linear Matrix Inequalities Applied to Stability Analysis and Synthesis of Controllers and Filters for Uncertain Dynamic Systems
Grantee:Pedro Luis Dias Peres
Support type: Regular Research Grants