Robust Model Predictive Control With Integral Sliding Mode in Continuous-Time Sampled-Data Nonlinear Systems
Robust Model Predictive Control With Integral Sliding Mode in Continuous-Time Sampled-Data Nonlinear Systems
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DOI：
10.1109/tac.2010.2074590
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发表时间：
2011-03
影响因子：
6.8
通讯作者：
Matteo Rubagotti;D. Raimondo;A. Ferrara;L. Magni
中科院分区：
文献类型：
--
作者：
Matteo Rubagotti;D. Raimondo;A. Ferrara;L. Magni
针对非线性约束连续时间不确定系统，提出了一种鲁棒模型预测控制（MPC）与滑模控制（SMC）相结合的控制策略。特别是，所谓的积分SMC的方法是用来产生一个控制动作，旨在减少标称预测动态的闭环系统和实际的一个之间的差异。以这种方式，MPC策略可以在具有减小的不确定性的系统上设计。为了证明整个控制方案的稳定性，一些一般的区域输入到状态的连续时间系统的实际稳定性结果进行了证明。
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.