When heavy-duty commercial vehicles(HDCVs)must engage in emergency braking,uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking control.To minimize these uncertain...When heavy-duty commercial vehicles(HDCVs)must engage in emergency braking,uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking control.To minimize these uncertainties,we propose a combined longitudinal and lateral controller method based on stochastic model predictive control(SMPC)that is achieved via Chebyshev–Cantelli inequality.In our method,SMPC calculates braking control inputs based on a finite time prediction that is achieved by solving stochastic programming elements,including chance constraints.To accomplish this,SMPC explicitly describes the probabilistic uncertainties to be used when designing a robust control strategy.The main contribution of this paper is the proposal of a braking control formulation that is robust against probabilistic friction circle uncertainty effects.More specifically,the use of Chebyshev–Cantelli inequality suppresses road profile influences,which have characteristics that are different from the Gaussian distribution,thereby improving both braking robustness and control performance against statistical disturbances.Additionally,since the Kalman filtering(KF)algorithm is used to obtain the expectation and covariance used for calculating deterministic transformed chance constraints,the SMPC is reformulated as a KF embedded deterministic MPC.Herein,the effectiveness of our proposed method is verified via a MATLAB/Simulink and TruckSim co-simulation.展开更多
为提升商用车的行驶安全性,本文基于触摸屏式新型人机交互系统,对商用车电控空气悬架(electronically controlled air suspension,ECAS)系统的故障诊断系统进行研究。针对ECAS故障诊断系统总体架构,提出了ECAS故障诊断及故障保护机制,...为提升商用车的行驶安全性,本文基于触摸屏式新型人机交互系统,对商用车电控空气悬架(electronically controlled air suspension,ECAS)系统的故障诊断系统进行研究。针对ECAS故障诊断系统总体架构,提出了ECAS故障诊断及故障保护机制,阐述了典型ECAS故障实例的诊断策略,并采用Matlab/Simulink搭建了诊断策略模型和故障码生成模型。为验证本文所提出的故障诊断及故障保护机制的可行性与实用性,以ECAS系统中压力传感器为例,对模型进行仿真分析和硬件在环试验。试验结果表明,在典型压力传感器故障工况下,本文所提出的ECAS故障诊断及故障保护机制,能够准确检测出相应故障,正确输出一系列相关信号,并在人机交互系统上将诊断结果进行实时显示。该研究对商用车ECAS人机交互系统的故障诊断系统设计开发具有一定的参考价值。展开更多
为了提升重型商用车的主动安全性能,以充分考虑车间信息的预碰撞时间作为碰撞风险指数设计碰撞风险评估模型。模型采用分级避撞控制策略,上层控制器根据碰撞风险指数决策出期望制动加速度,下层控制器通过比例积分微分(proportional inte...为了提升重型商用车的主动安全性能,以充分考虑车间信息的预碰撞时间作为碰撞风险指数设计碰撞风险评估模型。模型采用分级避撞控制策略,上层控制器根据碰撞风险指数决策出期望制动加速度,下层控制器通过比例积分微分(proportional integral differential,PID)调节上层输出的期望制动加速度,计算所需的制动压力,对车辆实施避撞控制。采用TruckSim和Simulink联合仿真对模型进行验证。结果表明:该分级避撞控制策略能快速识别碰撞风险,并及时制动,制动完成后和前车保持的距离为2.077~3.267 m,有效避免碰撞。展开更多
文摘When heavy-duty commercial vehicles(HDCVs)must engage in emergency braking,uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking control.To minimize these uncertainties,we propose a combined longitudinal and lateral controller method based on stochastic model predictive control(SMPC)that is achieved via Chebyshev–Cantelli inequality.In our method,SMPC calculates braking control inputs based on a finite time prediction that is achieved by solving stochastic programming elements,including chance constraints.To accomplish this,SMPC explicitly describes the probabilistic uncertainties to be used when designing a robust control strategy.The main contribution of this paper is the proposal of a braking control formulation that is robust against probabilistic friction circle uncertainty effects.More specifically,the use of Chebyshev–Cantelli inequality suppresses road profile influences,which have characteristics that are different from the Gaussian distribution,thereby improving both braking robustness and control performance against statistical disturbances.Additionally,since the Kalman filtering(KF)algorithm is used to obtain the expectation and covariance used for calculating deterministic transformed chance constraints,the SMPC is reformulated as a KF embedded deterministic MPC.Herein,the effectiveness of our proposed method is verified via a MATLAB/Simulink and TruckSim co-simulation.
文摘为提升商用车的行驶安全性,本文基于触摸屏式新型人机交互系统,对商用车电控空气悬架(electronically controlled air suspension,ECAS)系统的故障诊断系统进行研究。针对ECAS故障诊断系统总体架构,提出了ECAS故障诊断及故障保护机制,阐述了典型ECAS故障实例的诊断策略,并采用Matlab/Simulink搭建了诊断策略模型和故障码生成模型。为验证本文所提出的故障诊断及故障保护机制的可行性与实用性,以ECAS系统中压力传感器为例,对模型进行仿真分析和硬件在环试验。试验结果表明,在典型压力传感器故障工况下,本文所提出的ECAS故障诊断及故障保护机制,能够准确检测出相应故障,正确输出一系列相关信号,并在人机交互系统上将诊断结果进行实时显示。该研究对商用车ECAS人机交互系统的故障诊断系统设计开发具有一定的参考价值。
文摘为了提升重型商用车的主动安全性能,以充分考虑车间信息的预碰撞时间作为碰撞风险指数设计碰撞风险评估模型。模型采用分级避撞控制策略,上层控制器根据碰撞风险指数决策出期望制动加速度,下层控制器通过比例积分微分(proportional integral differential,PID)调节上层输出的期望制动加速度,计算所需的制动压力,对车辆实施避撞控制。采用TruckSim和Simulink联合仿真对模型进行验证。结果表明:该分级避撞控制策略能快速识别碰撞风险,并及时制动,制动完成后和前车保持的距离为2.077~3.267 m,有效避免碰撞。
