Data-driven Kalman Filter for Linear Continuous-time Parametric Uncertain Systems with Non-uniformly Sampled Data

摘要：

This paper develops one Kalman filtering technique for parametric uncertain continuous-time linear systems with non-uniformly sampled data. The considered problem is challenging in sense that normal Kalman filter is not applicable due to the unknown parameter in the system dynamics and the unknown parameter cannot be identified directly due to the lack of good state estimates. Based on a new discretization scheme addressing the known parameter and the non-uniformly sampled data, an algorithm based on Kalman filtering theory is proposed to estimate the uncertain parameter and states simultaneously, whose main idea is to merge the parameter estimation and state filtering in the same loop, that is to say, with the help of discretetime model obtained, the estimated states are used to estimate the parameter and the estimated parameter is fed into the state estimation. One typical numerical example is given to illustrate the feasibility and effectiveness of the proposed algorithm.

关键词： Non-uniformly Sampled Data Kalman Filter Parametric Uncertainty Discretization Scheme Simutaneously Estimating Parameter and States

作者: Pingsheng He Hongbin Ma Chenguang Yang Mengyin Fu

作者单位: School of Automation, Beijing Institute of Technology, Beijing 100081, China School of Automation, Beijing Institute of Technology, Beijing 100081, China Key Laboratory of Intel School of Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, United Kingdom

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 219-224

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）