Model Predictive Control for Nonlinear Affine Systems Based on the Simplified Dual Neural Network

被引：22
|
作者：
Pan, Yunpeng [1 ]
Wang, Jun [1 ]
机构：
[1] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China
来源：
2009 IEEE CONTROL APPLICATIONS CCA & INTELLIGENT CONTROL (ISIC), VOLS 1-3 | 2009年
关键词：
VARIATIONAL-INEQUALITIES; OPTIMIZATION PROBLEMS;
D O I：
10.1109/CCA.2009.5281106
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Model predictive control (MPC), also known as receding horizon control (RHC), is an advanced control strategy for optimizing the performance of control systems. For non-linear systems, standard MPC schemes based on linearization would result in poor performance. In this paper, we propose an MPC scheme for nonlinear affine systems based on a recurrent neural network (RNN) called the simplified dual network. The proposed RNN-based approach is efficient and suitable for real-time MPC implementation in industrial applications. Simulation results are provided to demonstrate the effectiveness and efficiency of the proposed MPC scheme.
引用
收藏
页码：683 / 688
页数：6
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