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基于自适应卡尔曼滤波的汽车前后轴干扰力估计 被引量:3

Vehicle Axles Disturbing Forces Estimation Based on Adaptive Kalman Filter
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摘要 针对汽车自动车道保持控制中汽车侧向干扰信息难以直接获取,提出把汽车动力学模型和自适应卡尔曼滤波理论相结合进行汽车前后轴干扰力估计。在线性二自由度汽车动力学模型中考虑前后轴的侧向力干扰,以辅助变量横摆角速度、侧向加速度和方向盘转角为量测信息,通过改进的自适应卡尔曼滤波算法建立了前后轴干扰力的最小方差估计,并对量测信号进行了滤波降噪。基于ADAMS/Car的虚拟试验验证了该算法具有较高的估计精度,可以为汽车侧向控制系统中估计器的设计提供理论指导。 To aim at the problem that lateral disturbance on vehicle are too difficult to measure directly in automatic lane keeping, vehicle axle disturbing forces estimation algorithm was proposed based on vehicle dynamics model and adaptive Kalmanfilter theory. Linear vehicle model of two degree-of-freedom was used, in which lateral forces on front and rear axle were considered. Assistant variables of yaw rate, lateral acceleration and steering wheel angle were considered as measurement information. Linear minimum variance estimation of axle disturbing forces was obtained by improved adaptive Kalman filter recursive algorithm, and the measurement signals were denoised. The virtual experiment implemented by ADAMS/Car indicates that the algorithm has high accuracy. It can provide theoretic direction for design of estimator in vehicle lateral control system in lane keeping.
出处 《系统仿真学报》 CAS CSCD 北大核心 2009年第5期1339-1342,共4页 Journal of System Simulation
基金 高等学校博士学科点专项科研基金项目(20040287004)
关键词 汽车前后轴干扰力 自适应卡尔曼滤波 动力学模型 状态估计 vehicle axles disturbing forces adaptive Kalman filter dynamic model state estimation
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参考文献8

  • 1Naab K, Reichart G.Drive assistance systems for lateral and longitudinal vehicle guidance-heading control and active cruise control [C]//Proc of 1994 AVEC. Japan: JSAE, 1994: 449-454.
  • 2Yamamoto M, Kagawa Y, Okuno A. Robust control for automated lane keeping against lateral disturbance [C]// International Conference on Intelligent Transportation Systems. Japan: IEEE, 1999: 240-245.
  • 3高振海,郑南宁,程洪.基于车辆动力学和Kalman滤波的汽车状态软测量[J].系统仿真学报,2004,16(1):22-24. 被引量:33
  • 4Best M, Gordon T, Dixon P. An extended adaptive Kalman filter for real-time estimation of vehicle handling dynamics [J], Vehicle System Dynamics (S0042-3114), 2000, 34(1): 57-75.
  • 5Arndt C, Karidas J, Busch R. Estimating non-measured vehicle states with an extended linearised Kalman filter [J]. Review of Automotive Engineering (S0389-4304), 2005, 26(1): 91-98.
  • 6Sage A P, Husa G W. Adaptive filtering with unknown prior statistics [C]//Proceedings of Joint Automatic Control Conference. USA: IEEE, 1969: 160-169.
  • 7张汉国,张洪钺.阻止自适应Kalman滤波发散的补救方法[J].控制与决策,1991,6(1):53-56. 被引量:31
  • 8宋文尧,张牙.卡尔曼滤波[M].北京:科学出版社,1988.

二级参考文献3

  • 1邓自立,王建国.带模型误差系统自适应Kalman滤波的虚拟噪声补偿技术[J]信息与控制,1988(01).
  • 2邓自立,郭一新.油田产油量、产水量动态预报[J]自动化学报,1983(02).
  • 3刘学军,张金换,黄世霖.汽车侧面碰撞模型的非线性动力学参数辨识[J].清华大学学报(自然科学版),1999,39(2):106-109. 被引量:4

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