基于CKF的大型拖拉机状态参数估计研究

doi:10.13733/j.jcam.issn.2095-5553.2024.04.017

摘要/Abstract

摘要： 拖拉机运行状态的准确识别与估计是其安全行驶和平稳控制的重要依据。针对大型拖拉机状态参数的估计复杂、精确度不高等问题，建立大型拖拉机整车三自由度仿真模型，其中包含Dugoff轮胎模型，提出基于容积卡尔曼滤波理论的大型拖拉机状态参数估计算法，并对大型拖拉机的行驶参数进行估计，包含纵向速度、侧向速度、质心侧偏角、横摆角速度。利用Matlab软件仿真验证，在双移线路面附着系数为0.8和0.6的工况下，对比仿真的状态参数和算法估计的数值。结果表明，拖拉机横摆角速度、质心侧偏角和纵向速度仿真值与真实值的误差分别为0.1、0.2、0.4，验证基于容积卡尔曼滤波算法对大型拖拉机状态参数估计的可行性和准确性，为大型拖拉机的稳定性控制等提供借鉴和参考。

关键词: 大型拖拉机, 非线性动力学, 容积卡尔曼滤波, 状态参数, Dugoff轮胎模型

Abstract: Accurate identification and estimation of tractor running state is an important basis for its safe driving and smooth control. Aiming at the problems of complex estimation and low accuracy of state parameters of large tractors, a three-degree-of-freedom simulation model of large tractors was established, including Dugoff tire model. The state parameter estimation algorithm of large tractor based on volume Kalman filter theory was proposed. Then, the driving parameters of large tractors were estimated, including longitudinal speed, lateral speed, sideslip angle of center of mass and yaw rate. Finally, the Matlab software was used to simulate and verify, and the simulated state parameters were compared with the values estimated by the algorithm under the condition that the adhesion coefficient of the double-shift line surface was 0.8 and 0.6. The results showed that the errors between the simulated values of yaw rate, sideslip angle of center of mass and longitudinal speed of tractor and the real values were 0.1, 0.2 and 0.4, respectively, which verified the feasibility and accuracy of estimating the state parameters of large tractors based on the volumetric Kalman filter algorithm, and provided reference for the stability control of large tractors.

Key words: large tractor, nonlinear dynamics, volumetric kalman filtering, state parameter, Dugoff tire model

中图分类号:

TH122
S220

魏国俊, 王鸿翔, 王圣杰, 王振雨, 肖茂华. 基于CKF的大型拖拉机状态参数估计研究[J]. 中国农机化学报, 2024, 45(4): 117-122.

Wei Guojun, Wang Hongxiang, Wang Shengjie, Wang Zhenyu, Xiao Maohua. Research on state parameter estimation of large tractor based on CKF[J]. Journal of Chinese Agricultural Mechanization, 2024, 45(4): 117-122.

参考文献

［1］　胡丹. 基于双扩展卡尔曼滤波的汽车状态及路面附着系数估计算法研究［D］. 长春: 吉林大学, 2009.Hu Dan. Research on the vehicle state and road tire friction coefficient eatimation based on dual extended kalman filter ［D］. Changchun: Jilin University, 2009.
［2］　时艳茹. 基于UKF滤波的汽车纵向和侧向速度估计算法研究［D］. 长春: 吉林大学, 2011.Shi Yanru. Vehicle longitudinal and lateral velocity estimation based on unscented Kalman filter ［D］. Changchun: Jilin University, 2011.
［3］　刘义, 杨鹏. 基于卡尔曼滤波的云台自适应姿态优化算法［J］. 自动化与仪表, 2022, 37(11): 80-86.
Liu Yi, Yang Peng. Adaptive attitude optimization algorithm of PTZ based on Kalman filter ［J］. Automation & Instrumentation, 2022, 37(11): 80-86.
［4］　Oei M, Sawodny O. Vehicle parameter estimation with Kalman filter disturbance observer ［J］. IFAC Papers On Line, 2022, 55(27): 497-502.
［5］　Sunusi I I, Zhou J, Wang Z Z, et al. Intelligent tractors: Review of online traction control process ［J］. Computers and Electronics in Agriculture, 2020, 170: 105176.
［6］　Kim J S, Lee D K, Ahn C K. Receding horizon directional unscented filter for heavyduty vehicles incorporating sensor modeling constraints ［J］. Measurement, 2021, 183： 109874.
［7］　李斌飞, 崔世钢, 施国英, 等. 基于无迹卡尔曼滤波的农用无人机定位研究［J］. 中国农机化学报, 2020, 41(9): 156-161.
Li Binfei, Cui Shigang, Shi Guoying, et al. Research on agricultural unmanned aerial vehicle positioning based on unscented Kalman filter ［J］. Journal of Chinese Agricultural Mechanization, 2020, 41(9): 156-161.
［8］　胡敬宇, 汪, 严永俊, 等. 基于限定记忆随机加权扩展卡尔曼滤波的车辆状态估计［J］. 东南大学学报(自然科学版), 2022, 52(2): 387-393.
Hu Jingyu, Wang Gong, Yan Yongjun, et al. Vehicle state estimation based on limited memory random weighted extended Kalman filter ［J］. Journal of Southeast University (Natural Science Edition), 2022, 52(2): 387-393.
［9］　林棻, 赵又群. 基于双重扩展自适应卡尔曼滤波的汽车状态和参数估计［J］. 中国机械工程, 2009, 20(6): 750-755.
Lin Fen, Zhao Youqun. Vehicle state and parameter estimation based on dual extended adaptive Kalman filter ［J］. China Mechanical Engineering, 2009, 20(6): 750-755.
［10］　Arasaratnam I, Haykin S. Cubature kalman filters ［J］. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269.
［11］　魏喜庆, 宋申民. 无模型容积卡尔曼滤波及其应用［J］. 控制与决策, 2013, 28(5): 769-773.
Wei Xiqing, Song Shenmin. Modelfree cubature Kalman filter and its application ［J］. Control and Decision, 2013, 28(5): 769-773.
［12］　Wenzel T A, Burnham K J, Blundell M V, et al. Dual extended Kalman filter for vehicle state and parameter estimation ［J］. Vehicle System Dynamics, 2006, 44(2): 153-171.
［13］　Ghansah B, Benuwa B B, Essel D D, et al. A Review of nonLinear kalman filtering for target tracking ［J］. International Journal of Data Analytics(IJDA), 2022, 3(1): 1-25.
［14］　Hahn J O, Rajamani R, Alexander L. GPS-based reaHtime identification of tireroad friction coefficient ［J］. IEEE Transactions on Control Systems Technology, 2002, 10(3): 331-343.
［15］　Tanelli M, Piroddi L, Savaresi S M. Realtime identification of tireroad friction conditions ［J］. IET Control Theory and Applications, 2009, 3(7): 891-906.
［16］　陈锦曦. 基于容积卡尔曼滤波的路面附着系数估计算法研究［D］. 成都: 电子科技大学, 2014.Chen Jinxi. Research on the algorithm of the road friction coefficient estimation based on the cubature Kalman filter ［D］. Chengdu: University of Electronic Science and Technology of China, 2014.
［17］　李刚, 解瑞春, 卫绍元, 等. 基于双容积卡尔曼滤波的车辆状态与路面附着系数估计［J］. 中国科学: 技术科学, 2015, 45(4): 403-414.
Li Gang, Xie Ruichun, Wei Shaoyuan, et al.Vehicle state and road friction coefficient estimation based on double cubature Kalman filter ［J］. China: Science of Technology, 2015, 45(4): 403-414.
［18］　宗新怡, 李刚, 邓伟文. 四轮独立驱动电动汽车车速估计研究［J］. 机械设计与制造, 2013(9): 83-85.
Zong Xinyi, Li Gang, Deng Weiwen.Study on velocity estimation of fourwheel independent drive electric vehicle ［J］. Mechanical Design and Manufacturing, 2013(9): 83-85.
［19］　汪涛. 面向商用车的路面附着系数估计研究［D］. 重庆: 重庆邮电大学, 2019.Wang Tao. Estimation of road adhesion coefficient for commercial vehicles ［D］. Chongqing: Chongqing University of Posts and Telecommunications, 2019.