期刊
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
卷 50, 期 1, 页码 141-151出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2018.1544303
关键词
Parameter estimation; hierarchical principle; iterative estimation; Newton search; least squares
类别
资金
- National Natural Science Foundation of China [6187311]
- 111 Project [B12018]
- National First-Class Discipline Program of Light Industry Technology and Engineering [LITE2018-26]
- Qing Lan Project
- Postdoctoral Science Foundation of Jiangsu Province [1701020A]
This paper develops a parameter estimation algorithm for linear continuous-time systems based on the hierarchical principle and the parameter decomposition strategy. Although the linear continuous-time system is a linear system, its output response is a highly nonlinear function with respect to the system parameters. In order to propose a direct estimation algorithm, a criterion function is constructed between the response output and the observation output by means of the discrete sampled data. Then a scheme by combining the Newton iteration and the least squares iteration is builded to minimise the criterion function and derive the parameter estimation algorithm. In light of the different features between the system parameters and the output function, two sub-algorithms are derived by using the parameter decomposition. In order to remove the associate terms between the two sub-algorithms, a Newton and least squares iterative algorithm is deduced to identify system parameters. Compared with the Newton iterative estimation algorithm without the parameter decomposition, the complexity of the hierarchical Newton and least squares iterative estimation algorithm is reduced because the dimension of the Hessian matrix is lessened after the parameter decomposition. The experimental results show that the proposed algorithm has good performance.
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