Model Predictive Control Integrated With Multi-Agent Particle Swarm Optimization-based SVR

被引：0
|
作者：
Tang, Xian-lun [1 ]
Liu, Nian-ci [2 ]
Wan, Ya-li [1 ]
Lin, Wen-xing [1 ]
机构：
[1] Chongqing Univ Posts & Telecommun, Coll Automat, Chongqing 400065, Peoples R China
[2] ZBJ Network Inc, Chongqing 401121, Peoples R China
来源：
2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年
关键词：
support vector regression; polynomial kernel; nonlinear model predictive control; multi-agent particle swarm optimization;
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：
The kernel function's selection has a great impact on the performance of support vector regression (SVR). A new method of nonlinear model predictive control (NMPC) based on polynomial kernel SVR is put forward, and multi-agent particle swarm optimization algorithm is introduced to obtain the optimal control law of rolling optimization in NMPC. Compares with the NMPC based on quadratic polynomial kernel SVR, the simulation results show that the characteristics of our method, such as, overshoot, volatility and tracking are superior to those of quadratic polynomial kernel SVR.
引用
收藏
页码：929 / 935
页数：7
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