基于高斯过程回归的车辆巡航系统学习预测控制

doi:10.16183/j.cnki.jsjtu.2020.173

摘要/Abstract

摘要：

针对自动巡航系统中前车加速度预测问题,以及为满足人们对车辆安全性、舒适性和经济性要求,提出一种基于高斯过程回归的车辆自动巡航系统学习预测控制策略.先用高斯过程回归法对前车加速度做学习建模,再结合车间运动学模型定义车辆自动巡航系统预测模型.进而,通过在线滚动优化车辆自动巡航系统安全性、舒适性和经济性综合指标,建立车辆自动巡航系统学习预测控制器.最后,通过CarSim/Simulink联合仿真平台,将本方法的加减速典型驾驶工况与传统预测巡航控制策略下的驾驶工况对比验证.结果表明:与传统控制策略相比,本文方法更具有效性和优越性.

关键词: 模型预测控制, 高斯过程回归, 自动巡航系统, 自主车辆

Abstract:

Aimed at the preceding vehicular acceleration prediction problem in automated cruise systems, a learning predictive control strategy is proposed based on Gaussian process regression to meet people’s requirements for safety, comfort, and economy of vehicles. First, the method of Gaussian process regression is used to build the learning modeling of preceding vehicular acceleration. Then, the learning model is combined with the inter-vehicle kinematics models to define the predictive model of the vehicular automated cruise system. After that, the learning predictive controller is estabilished for the vehicular automated cruise system through optimizing the safety, driving comfort, and economy indexes online. Finally, under accelerating-decelerating classical driving scenarios, the effectiveness of the method proposed is compared with that of the traditional predictive cruise control on the CarSim/Simulink co-simulation platform. The results show that the method proposed is more effective and superior to traditional control strategies.

Key words: model predictive control, Gaussian process regression, automated cruise systems, automated vehicles

中图分类号:

TP273

何德峰, 彭彬彬, 顾煜佳, 余世明. 基于高斯过程回归的车辆巡航系统学习预测控制[J]. 上海交通大学学报, 2020, 54(9): 904-909.

HE Defeng, PENG Binbin, GU Yujia, YU Shiming. Learning Predictive Control of Vehicular Automated Cruise Systems Based on Gaussian Process Regression[J]. Journal of Shanghai Jiaotong University, 2020, 54(9): 904-909.

图/表 5

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