Stochastic Model Predictive Control of Markov Jump Linear Systems Based on a Two-layer Recurrent Neural Network

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：
2013 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) | 2013年
关键词：
OPTIMIZATION SUBJECT; NONLINEAR-SYSTEMS; STABILITY; MPC;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a stochastic model predictive control approach to constrained Markov jump linear systems based on neurodynamic optimization. The stochastic model predictive control problem is formulated as a nonlinear convex optimization problem, which is iteratively solved by using a two-layer recurrent neural network in real-time. The applied neural network can globally converge to the exact optimal solution of the optimization problem. Simulation results are provided to demonstrate the effectiveness and characteristics of the proposed approach.
引用
收藏
页码：564 / 569
页数：6
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