基于多智能体模型的动车组分布式预测控制

李中奇; 杨辉; 张坤鹏; 付雅婷

doi:10.3724/SP.J.1004.2014.02625

基于多智能体模型的动车组分布式预测控制

doi: 10.3724/SP.J.1004.2014.02625

李中奇^1,2,3,
杨辉^1,2,3,
张坤鹏^2,3,
付雅婷^2,3

1.
南昌大学机电工程学院南昌 330031;
2.
华东交通大学电气与电子工程学院南昌 330013;
3.
江西省先进控制与优化重点实验室南昌 330013

基金项目:

Supported by National Natural Science Foundation of China (61164013, U1334211, 51174091), the Key Program of China Ministry of Railway (2011Z002-D), and Natural Science Foundation of Jiangxi Province (20122BAB201021)

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- 文章访问数: 1967
- HTML全文浏览量: 110
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- 被引次数: 0
出版历程
- 收稿日期: 2013-06-17
- 修回日期: 2013-11-12
- 刊出日期: 2014-11-20

Distributed Model Predictive Control Based on Multi-agent Model for Electric Multiple Units

LI Zhong-Qi^1,2,3,
YANG Hui^1,2,3,
ZHANG Kun-Peng^2,3,
FU Ya-Ting^2,3

1.
School of Mechanical and Electrical Engineer, Nanchang University, Nanchang 330031, China;
2.
School of Electrical and Electronic Engineering, East China Jiaotong University, Nanchang 330013, China;
3.
Key Laboratory of Advanced Control and Optimization of Jiangxi Province, Nanchang 330013, China

Funds:

摘要

摘要: 以高速铁路普遍采用的动力分散式动车组为研究对象.针对动车组由若干动力单元相互耦合组成的结构特点,以各动力单元为智能体.结合动车组牵引特性曲线和实际运行数据,应用减法聚类和模式分类算法建立各智能体多模型集.依据各智能体网络拓扑结构和相互耦合约束关系,建立动车组多智能体模型.针对各智能体的耦合约束,采用PID和GPC相结合的平稳起动切换控制策略和基于邻域优化的多智能体分布式协调控制算法,实现各智能体对给定速度的同步跟踪.基于CRH380A型动车组运行数据的仿真结果验证了本文方法的有效性.
- 动车组 /
- 多智能体 /
- 非线性 /
- 多模型 /
- 分布式预测控制 /
- 同步跟踪
Abstract: The distributed-power electric multiple units (EMUs) are widely used in high-speed railway. Due to the structural characteristic of mutual-coupled power units in EMUs, each power unit is set as an agent. Combining with the traction/brake characteristic curve and running data of EMUs, a subtractive clustering method and pattern classification algorithm are adopted to set up a multi-model set for every agent. Then, the multi-agent model is established according to the multi-agent network topology and mutual-coupled constraint relations. Finally, we adopt a smooth start switching control strategy and a multi-agent distributed coordination control algorithm to ensure the synchronous speed tracking control of each agent. Simulation results on the actual CRH380A running data show the effectiveness of the proposed approach.
- Electric multiple units (EMUs) /
- multi-agent /
- nonlinear /
- multi-models /
- distributed model predictive control /
- synchronous tracking

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参考文献(25)

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