State Observer Design Based on a Model of Chemostat Microbial Cultivation Accounting for the Memory Effects

2003 ◽  
Vol 42 (12) ◽  
pp. 1807-1827
Author(s):  
Olga Georgieva ◽  
Trayana Patarinska
2021 ◽  
Vol 22 (8) ◽  
pp. 404-410
Author(s):  
K. B. Dang ◽  
A. A. Pyrkin ◽  
A. A. Bobtsov ◽  
A. A. Vedyakov ◽  
S. I. Nizovtsev

The article deals with the problem of state observer design for a linear time-varying plant. To solve this problem, a number of realistic assumptions are considered, assuming that the model parameters are polynomial functions of time with unknown coefficients. The problem of observer design is solved in the class of identification approaches, which provide transformation of the original mathematical model of the plant to a static linear regression equation, in which, instead of unknown constant parameters, there are state variables of generators that model non-stationary parameters. To recover the unknown functions of the regression model, we use the recently well-established method of dynamic regressor extension and mixing (DREM), which allows to obtain monotone estimates, as well as to accelerate the convergence of estimates to the true values. Despite the fact that the article deals with the problem of state observer design, it is worth noting the possibility of using the proposed approach to solve an independent and actual estimation problem of unknown time-varying parameters.


2012 ◽  
Vol 36 (12) ◽  
pp. 1543-1549
Author(s):  
Hyun Seok Kang ◽  
Wan Kee Hong ◽  
I Cheol Hwang

2018 ◽  
Vol 469 ◽  
pp. 30-43 ◽  
Author(s):  
Shunyuan Xiao ◽  
Yijun Zhang ◽  
Baoyong Zhang

2020 ◽  
Vol 28 (3) ◽  
pp. 1149-1157
Author(s):  
Ji Wang ◽  
Yangjun Pi ◽  
Yumei Hu ◽  
Zhencai Zhu

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