摘要
对于因模型参数失配造成的非线性系统状态估计不准确现象,采用基于不敏卡尔曼滤波(UKF)的参数和状态联合估计方法,即将未知模型参数和状态组成增广的状态向量,用UKF同时获得参数和状态估计值。通过一个离散非线性随机系统的蒙特-卡洛仿真,总结滤波器参数对联合估计器性能的影响及参数选择规律。最后将该方法应用于一个典型的化工反应过程,获得了较好的效果。
The mismatch of model parameter would lead to the inaccuracy of the estimation of states for nonlinear systems. This paper proposes a joint estimation approach based on Unscented Kalman Filter, in which both parameters and states are simultaneously estimated by means of the argument state vector composed of the unknown model parameters and states. By utilizing the Monte Carlo simulation for a discrete nonlinear stochastic system, the influence of the filter parameters on the performance of the joint estimator is analyzed and the updating rule of filter parameters is obtained. Finally, this approach is successfully applied to a typical chemical reaction process.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2009年第5期762-767,共6页
Journal of East China University of Science and Technology
基金
上海市基础研究重点项目(08JC1408200)
上海市重点学科建设项目(B504)
国家"863"高技术研究发展计划项目(2009AA04Z141)
关键词
模型失配
不敏卡尔曼滤波
联合估计
滤波器参数
model mismatch
unscented Kalman filter
joint estimation
filter parameter
