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http://dx.doi.org/10.5370/KIEE.2018.67.2.248

Experimental Study on a Monte Carlo-based Recursive Least Square Method for System Identification  

Lee, Sang-Deok (Dept. of Mechatronics Engineering, Chungnam National University)
Jung, Seul (Dept. of Mechatronics Engineering, Chungnam National University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.67, no.2, 2018 , pp. 248-254 More about this Journal
Abstract
In this paper, a Monte Carlo-based Recursive Least Square(MC-RLS) method is presented to directly identify the inverse model of the dynamical system. Although a RLS method has been used for the identification based on the deterministic data in the closed loop controlled form, it would be better for RLS to identify the model with random data. In addition, the inverse model obtained by inverting the identified forward model may not work properly. Therefore, MC-RLS can be used for the inverse model identification without proceeding a numerical inversion of an identified forward model. The performance of the proposed method is verified through experimental studies on a control moment gyroscope.
Keywords
Recursive least square; Monte carlo simulation; inverse model identification; Control moment gyroscope;
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