.png){kind=icon}
返回
Output-Tracking Quantized Explicit Nonlinear Model Predictive Control Using Multiclass Support Vector Machines
.png){kind=icon}
PRE
AI
DOI：10.1109/TIE.2016.2638401.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In applications involving digital control, the set of admissible control actions is finite/quantized. Coupled with state constraints and fast dynamics, explicit model predictive control (EMPC) provides an attractive control formalism. However, the design of data-driven EMPCs with finite admissible control sets is a challenging and relatively unexplored problem. In this paper, a systematic data-driven method is proposed for the design of quantized EMPCs (Q-EMPCs) for time-varying output tracking in nonlinear systems. The design involves: 1) sampling the admissible state space using low-discrepancy sequences to provide scalability to higher dimensional nonlinear systems; 2) at each sampled data point, solving for optimal quantized model predictive control actions and determining feasibility of the intrinsic mixed-integer nonlinear programming problem; and 3) constructing the Q-EMPC control surface using multiclass support vector machines (MC-SVMs). In particular, four widely used MC-SVM algorithms are employed to construct the proposed data-driven Q-EMPC. Extensive testing and comparison among the different MC-SVM algorithms is performed on 2-D and 5-D benchmark examples to demonstrate the effectiveness and scalability of the proposed methodology.
Keyword:
Explicit nonlinear model predictive control
finite control set
multicategory learning
output tracking
supervised learning
support vector machines (SVMs)
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:
7.2
论文数:
1.8W
被引数:
9.8W
机构
引用论文
Effect of Recycling on the Mechanical, Thermal and Rheological Properties of Polypropylene/Carbon Nanotube Composites.png)
Polymers
OAAI
PolymersIF0
OAAI分享收藏分享收藏分享收藏
查看更多内容