Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic mobility.Whereas the effects of inertial parameter variation in DDEV control s...Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic mobility.Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size,and inertial parameter has seldom been tackled and systematically estimated.This paper presents a dual central difference Kalman filter(DCDKF)where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters,such as vehicle sideslip angle,vehicle mass,vehicle yaw moment of inertia,the distance from the front axle to centre of gravity.The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs.The four-wheel nonlinear vehicle dynamics estimation model considering payload variations,Pacejka tire model,wheel and motor dynamics model is developed,the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory.To address system nonlinearities in vehicle dynamics estimation,the DCDKF and dual extended Kalman filter(DEKF)are also investigated and compared.Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-CarsimR.The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions.This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.展开更多
针对移动机器人同时定位与地图创建(Simultaneous localization and mapping,SLAM)中的FastSLAM算法,存在非线性系统线性化处理和计算雅可比矩阵的缺点,本文提出了基于Sterling多项式插值处理非线性系统的SLAM方法.该方法基于Rao-Blackw...针对移动机器人同时定位与地图创建(Simultaneous localization and mapping,SLAM)中的FastSLAM算法,存在非线性系统线性化处理和计算雅可比矩阵的缺点,本文提出了基于Sterling多项式插值处理非线性系统的SLAM方法.该方法基于Rao-Blackwellized粒了滤波框架,利用中心差分滤波方法产生改进的建议分布函数,提高了机器人位姿估计的精度;利用中心差分滤波初始化特征和更新地图中的特征,提高了地图创建的精度;针对实际应用中存在虚假特征的情况提出了一种有效的地图管理方法.在同等粒了数的情况下,该方法改进了SLAM结果的精度.基于仿真和实际数据的实验结果验证了该方法的有效性.展开更多
粒子滤波器能够给出移动机器人全局定位非线性非高斯模型的近似解.然而,当新感知出现在先验概率的尾部或者与先验相比感知概率太尖时,传统的粒子滤波器会退化导致定位失败.本文提出了一种重要性采样跟中心差分滤波器(cen tra l d iffere...粒子滤波器能够给出移动机器人全局定位非线性非高斯模型的近似解.然而,当新感知出现在先验概率的尾部或者与先验相比感知概率太尖时,传统的粒子滤波器会退化导致定位失败.本文提出了一种重要性采样跟中心差分滤波器(cen tra l d ifference filter,CDF)相结合的新算法,并对测量更新步的加权粒子集应用基于KD-树的加权期望最大(w e igh ted expecta tion m ax im iza tion,W EM)自适应聚类算法获得表示机器人位姿状态后验密度的高斯混合模型(G au ssian m ixtu re m od e l,GMM).实验结果表明,新方法提高了定位准确率,降低了计算复杂度.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.51905329,51975118)Foundation of State Key Laboratory of Automotive Simulation and Control of China(Grant No.20181112).
文摘Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic mobility.Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size,and inertial parameter has seldom been tackled and systematically estimated.This paper presents a dual central difference Kalman filter(DCDKF)where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters,such as vehicle sideslip angle,vehicle mass,vehicle yaw moment of inertia,the distance from the front axle to centre of gravity.The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs.The four-wheel nonlinear vehicle dynamics estimation model considering payload variations,Pacejka tire model,wheel and motor dynamics model is developed,the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory.To address system nonlinearities in vehicle dynamics estimation,the DCDKF and dual extended Kalman filter(DEKF)are also investigated and compared.Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-CarsimR.The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions.This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.
文摘针对移动机器人同时定位与地图创建(Simultaneous localization and mapping,SLAM)中的FastSLAM算法,存在非线性系统线性化处理和计算雅可比矩阵的缺点,本文提出了基于Sterling多项式插值处理非线性系统的SLAM方法.该方法基于Rao-Blackwellized粒了滤波框架,利用中心差分滤波方法产生改进的建议分布函数,提高了机器人位姿估计的精度;利用中心差分滤波初始化特征和更新地图中的特征,提高了地图创建的精度;针对实际应用中存在虚假特征的情况提出了一种有效的地图管理方法.在同等粒了数的情况下,该方法改进了SLAM结果的精度.基于仿真和实际数据的实验结果验证了该方法的有效性.
文摘粒子滤波器能够给出移动机器人全局定位非线性非高斯模型的近似解.然而,当新感知出现在先验概率的尾部或者与先验相比感知概率太尖时,传统的粒子滤波器会退化导致定位失败.本文提出了一种重要性采样跟中心差分滤波器(cen tra l d ifference filter,CDF)相结合的新算法,并对测量更新步的加权粒子集应用基于KD-树的加权期望最大(w e igh ted expecta tion m ax im iza tion,W EM)自适应聚类算法获得表示机器人位姿状态后验密度的高斯混合模型(G au ssian m ixtu re m od e l,GMM).实验结果表明,新方法提高了定位准确率,降低了计算复杂度.
