Paper Info
Title
Knowledge-Data-Driven Model Predictive Control for a Class of Nonlinear Systems
Abstract
Model predictive control (MPC) has been considered as a promising alternative for the control of nonlinear systems. However, this controller suffers from a challenge that it is difficult to deal with the complex nonlinear systems with incomplete datasets. To solve this problem, a novel MPC, by utilizing knowledge-data-driven model (KDDM), is designed and analyzed in this article. In comparison wit...
Year
DOI
Venue
2021
10.1109/TSMC.2019.2937002
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Nonlinear systems,Computational modeling,Optimal control,Optimization,Predictive control,Adaptation models
Journal
51
Issue
ISSN
Citations 
7
2168-2216
0
PageRank 
References 
Authors
0.34
20
4
Name
Order
Citations
PageRank
Hong-Gui Han147639.06
Zheng Liu200.34
Hong-Xu Liu392.81
Jun-Fei Qiao46915.62