铁道标准设计Issue(6)：143-147,5.DOI:10.13238/j.issn.1004-2954.2015.06.032
基于粒子群算法的隐式广义预测在ATO中的应用
Application of Implicit Generalized Prediction Based on Particle Swarm Optimization algorithm in ATO
摘要
Abstract
The continuous increase of train speed sets higher requirements for the Automatic Train Operation ( ATO) system. As it is difficult to obtain the optimal predictive control input for the implicit generalized predictive controller in the automatic train operation, this paper applies an IGPC algorithm based on Particle Swarm Optimization ( PSO) to control the ATO system. In order to further optimize PSO, the basic PSO algorithm is improved, thus effectively improving the accuracy and speed of searching optimization. The CHR2 trains are simulated and verified under constrained conditions. The simulation results show that PSO-IGPC has a better effect than simple IGPC control for ATO.关键词
列车自动驾驶/隐式广义预测控制算法/粒子群优化算法/仿真Key words
Automatic train operation/Implicit generalized predictive control algorithm/Particle swarm分类
信息技术与安全科学引用本文复制引用
马宝峰,路小娟..基于粒子群算法的隐式广义预测在ATO中的应用[J].铁道标准设计,2015,(6):143-147,5.基金项目
甘肃省自然科学基金(1208RJZA180) （1208RJZA180）
