Sampled-data model predictive control for nonlinear time-varying systems: Stability and robustness

• Type: Article in International Conference Proceedings Book
• Date: 2007

Authors

fontes, facc (Author) Other; magni, l (Author) Other; gyurkovics, e (Author) Other

Conference proceedings International

Title: Assessment and Future Directions of Nonlinear Model Predictive Control

Pages: 115-129

International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control

Freudenstadt Lauterbad, GERMANY, AUG 26-30, 2005

Indexing

ISI Web of Knowledge - 15 Citations

Scopus - 19 Citations

Other information

Authenticus ID: P-004-CJV

DOI: 10.1007/978-3-540-72699-9_9

Abstract (EN): We describe here a sampled-data Model Predictive Control framework that uses continuous-time models but the sampling of the actual state of the plant as well as the computation of the control laws, are carried out at discrete instants of time. This framework can address a very large class of systems, nonlinear, time-varying, and nonholonomic. As in many others sampled-data Model Predictive Control schemes, Barbalat's lemma has an important role in the proof of nominal stability results. It is argued that the generalization of Barbalat's lemma, described here, can have also a similar role in the proof of robust stability results, allowing also to address a very general class of nonlinear, time-varying, nonholonomic systems, subject to disturbances. The possibility of the framework to accommodate discontinuous feedbacks is essential to achieve both nominal stability and robust stability for such general classes of systems.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15