Sampled-Data Model Predictive Control

• 成果类型: Article
• 署名作者: Geromel, Jose C.
• 署名单位: Universidade Estadual de Campinas
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3077353
• 发表日期: 2022
• 页码: 2466-2472
• 关键词: Differential linear matrix inequality (DLMI); model predictive control (MPC); sampled-data control

摘要

This article focuses on model predictive control (MPC) design in the context of sampled-data control systems with full-state measurements. It is shown that recent results on this area can be successfully generalized to cope with sampled-data MPC. The open-loop plant is subjected to polytopic parameter uncertainty and at sampling times, a controlled output variable satisfies a set of convex constraints. A guaranteed H-2 performance index with infinity horizon is minimized such that the feedback control preserves asymptotic stability and feasibility. The design conditions are expressed through differential linear matrix inequalities. Continuous-time systems are treated with no kind of discrete-time modeling approximation. Comparisons with classical methods from the literature dealing with continuous-time systems are presented and discussed. Examples are included for illustration.