.png){kind=icon}
返回
Robust MPC design for multi-model infinite-dimensional distributed parameter systems
.png){kind=icon}
OA
AI
DOI：10.1016/j.jprocont.2024.103316.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Infinite-dimensional systems are essential for describing complex phenomena that exhibit continuous spatial and temporal variations. This article introduces a robust model predictive control (RMPC) design to regulate constrained multi-model infinite-dimensional systems governed by a class of hyperbolic/parabolic partial differential equations (PDEs). Model uncertainty stems from system parameters that are imprecisely determined, but can be quantitatively characterized within a certain range. The RMPC algorithm is designed in a discrete-time infinite-dimensional setting, achieved through the structure-preserving Cayley-Tustin transformation without model reduction nor spatial approximation. Robustness of the controller is ensured via constraining the future cost for each model dynamics accounting for uncertainty description. Properties of the closed-loop system are discussed, including feasibility, convergence, and asymptotic stability. The proposed controller is implemented by considering three typical infinite-dimensional distributed parameter process models, with simulation demonstrating the effectiveness and enhanced performance of the RMPC over the nominal model predictive controller.
Keyword:
Distributed parameter systems
Partial differential equations
Robust model predictive control
Multi-model uncertainty
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
3.5K
被引数:
7.3K
机构
引用论文
A robustly stabilizing model predictive control strategy of stable and unstable processes.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏分享收藏
查看更多内容