A Neural Network Approach to Nonlinear Model Predictive Control

被引：0
|
作者：
Yan, Zheng [1 ]
Wang, Jun [1 ]
机构：
[1] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China
来源：
IECON 2011: 37TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2011年
关键词：
QUADRATIC-PROGRAMMING PROBLEMS; OPTIMIZATION; REDUNDANCY; SUBJECT;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This paper proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC problem is formulated as a convex programming problem via Jacobain linearization. The unknown high-order term associated with the linearization is estimated by using a feedforward neural network via supervised learning. The convex optimization problem involved in MPC is solved by using a recurrent neural network. Simulation results are provided to demonstrate the performance of the approach.
引用
收藏
页码：2305 / 2310
页数：6
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