A Predictor Form State-Space Identification Algorithm Using Multivariate Linear Regression

摘要：

This paper describes a novel MIMO state-space identification algorithm that is based on multivariate linear regression rather than the usual subspace techniques such as orthogonal and oblique projection. We first estimate the Markov parameters of the predictor using multivariate regression, then the state sequence is estimated using singular value decomposition via an equation central to our approach, and finally the A,B,C,K matrices are computed again by multivariate regression. Our algorithm is in predictor form, so it is suitable for both open- and closed-loop cases. Numerical experiments show the accuracy of our algorithm.

关键词： State-Space Identification Multivariate Linear Regression Subspace Identification Closed-Loop Identification

作者: Yiping CHENG

作者单位: Advanced Control Systems Laboratory School of Electronic and Information Engineering Beijing Jiaotong University, Beijing 100044, China

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: 1877-1880

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）