Multi-Step Model Predictive Control Based on Online Support Vector Regression Optimized by Multi-Agent Particle Swarm Optimization Algorithm

doi:10.1007/s12204-018-1990-1

Journal of Shanghai Jiao Tong University (Science) ›› 2018, Vol. 23 ›› Issue (5): 607-612.doi: 10.1007/s12204-018-1990-1

Multi-Step Model Predictive Control Based on Online Support Vector Regression Optimized by Multi-Agent Particle Swarm Optimization Algorithm

TANG Xianlun (唐贤伦), LIU Nianci (刘念慈), WAN Yali (万亚利), GUO Fei (郭飞)

(1. College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; 2. Chongqing Financial Assets Exchange, Chongqing 400010, China)

出版日期:2018-10-01 发布日期:2018-10-07
通讯作者: TANG Xianlun (唐贤伦) E-mail:tangxl@cqupt.edu.cn

Multi-Step Model Predictive Control Based on Online Support Vector Regression Optimized by Multi-Agent Particle Swarm Optimization Algorithm

TANG Xianlun (唐贤伦), LIU Nianci (刘念慈), WAN Yali (万亚利), GUO Fei (郭飞)

(1. College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; 2. Chongqing Financial Assets Exchange, Chongqing 400010, China)

Online:2018-10-01 Published:2018-10-07
Contact: TANG Xianlun (唐贤伦) E-mail:tangxl@cqupt.edu.cn

摘要/Abstract

摘要： As optimization of parameters affects prediction accuracy and generalization ability of support vector regression (SVR) greatly and the predictive model often mismatches nonlinear system model predictive control, a multi-step model predictive control based on online SVR (OSVR) optimized by multi-agent particle swarm optimization algorithm (MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.

关键词: online support vector regression (OSVR), model predictive controller (MPC), multi-agent particle swarm optimization (MAPSO), nonlinear systems

Abstract: As optimization of parameters affects prediction accuracy and generalization ability of support vector regression (SVR) greatly and the predictive model often mismatches nonlinear system model predictive control, a multi-step model predictive control based on online SVR (OSVR) optimized by multi-agent particle swarm optimization algorithm (MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.

Key words: online support vector regression (OSVR), model predictive controller (MPC), multi-agent particle swarm optimization (MAPSO), nonlinear systems

中图分类号:

TP 13

TANG Xianlun (唐贤伦), LIU Nianci (刘念慈), WAN Yali (万亚利), GUO Fei (郭飞). Multi-Step Model Predictive Control Based on Online Support Vector Regression Optimized by Multi-Agent Particle Swarm Optimization Algorithm[J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(5): 607-612.

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Multi-Step Model Predictive Control Based on Online Support Vector Regression Optimized by Multi-Agent Particle Swarm Optimization Algorithm

Multi-Step Model Predictive Control Based on Online Support Vector Regression Optimized by Multi-Agent Particle Swarm Optimization Algorithm

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