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

被引：153

作者：

Rubagotti, Matteo ^{[1
]}

Raimondo, Davide Martino ^{[2
]}

Ferrara, Antonella ^{[3
]}

Magni, Lalo ^{[3
]}

机构：

[1] Univ Trento, Dept Mech & Struct Engn, I-38123 Trento, Italy

[2] Swiss Fed Inst Technol, Automat Control Lab, CH-8092 Zurich, Switzerland

[3] Univ Pavia, Dept Comp Engn & Syst Sci, I-27100 Pavia, Italy

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2011年 / 56卷 / 03期

关键词：

Constrained control; nonlinear predictive control (NPC); sampled data control; sliding mode control (SMC); stability of nonlinear systems; TO-STATE STABILITY; INPUT; DESIGN;

D O I：

10.1109/TAC.2010.2074590

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：556 / 570

页数：15

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