Closed-Loop Identification of Multivariable Equation Error Model with Unknown Disturbances
Closed-loop identification for industrial processes has been widely studied in the past decades. Multivariable equation error models (ARX) are frequently used for closedloop modeling with input and output measurements. When unmeasured disturbances (errors) exist in the equation error models, the traditional prediction error methods are used to identify the models by treating the disturbances as filtered white noises, which is not the case for many disturbances and thus seriously deteriorating the estimation results. In this paper, a recursive least squares estimation with unknown disturbances (RLSE-UD) approach is introduced to estimate the parameters of multivariable equation error models, as well as the unmeasured disturbances. No prior information of the unknown disturbances is required for RLSE-UD and the estimator is proven unbiased and consistent. Simulation results demonstrate that the RLSE-UD approach is capable of identifying the parameters of multivariable equation error models and unmeasured disturbances in closed-loop cases well.
Shuwen Pan Mengjia Shi
Institute of Cyber-Systems and Control,Yuquan Campus, Zhejiang University, Hangzhou 310027, China Zhejiang University of Science and Technology, Hangzhou 310023, China
国际会议
2011 International Symposium on Advanced Control of Industrial Processes(2011工业过程先进控制技术国际研讨会)
杭州
英文
638-643
2011-05-01（万方平台首次上网日期，不代表论文的发表时间）