| Full text | |
| Author(s): |
Massera, Carlos M.
;
Terra, Marco H.
;
Wolf, Denis F.
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 2017 AMERICAN CONTROL CONFERENCE (ACC); v. N/A, p. 6-pg., 2017-01-01. |
| Abstract | |
In this paper we propose a constrained guaranteed cost robust model predictive controller (GCMPC) for uncertain discrete time systems. This controller was developed based on a quadratic cost functional and guarantee robustness with respect to quadratically bound uncertainties. Such a class of problems is currently intractable by Min-Max Robust Model Predictive Controllers without polytopic approximations of the uncertainties. The proposed technique is computationally more efficient then an enumeration-based approach and requires only a Quadratically Constrained Quadratic Problem (QCQP) optimization, whereas LMI-based GCMPC approaches require a Semi-Definite Programming (SDP) optimization. (AU) | |
| FAPESP's process: | 13/24542-7 - Project CARINA: localization and control |
| Grantee: | Denis Fernando Wolf |
| Support Opportunities: | Regular Research Grants |