Work

Robust Model Predictive Control With Integral Sliding Mode in Continuous-Time Sampled-Data Nonlinear Systems

Year: 2010

Type: article

Abstract: This paper proposes a control strategy for nonlinear constrained continuous-time uncertain systems which combines robust model predictive control (MPC) with sliding mode control (SMC). In particular, the so-called Integral SMC approach is used to produce a control action aimed to reduce the difference between the nominal predicted dynamics of the closed-loop system and the actual one. In this way, the MPC strategy can be designed on a system with a reduced uncertainty. In order to prove the stability of the overall control scheme, some general regional input-to-state practical stability results for continuous-time systems are proved.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2010.2074590

Authors: Matteo Rubagotti, Davide Martino Raimondo, Antonella Ferrara, Lalo Magni

Institutions: University of Trento, ETH Zurich, Automatic Control Laboratory, University of Pavia

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Università degli Studi di Pavia, FP7 Information and Communication Technologies

Awards: G2203972493, Feedback design for wireless networked systems