Regular Papers

International Journal of Control, Automation and Systems 2022; 20(12): 3940-3950

Published online December 10, 2022

https://doi.org/10.1007/s12555-021-0845-y

© The International Journal of Control, Automation, and Systems

Hierarchical Recursive Least Squares Estimation Algorithm for Second-order Volterra Nonlinear Systems

Jian Pan*, Sunde Liu, Jun Shu, and Xiangkui Wan

Hubei University of Technology

Abstract

This paper considers the parameter identification problems of a Volterra nonlinear system. In order to overcome the excessive calculation amount of the Volterra systems, a hierarchical least squares algorithm is proposed through combining the hierarchical identification principle. The key is to decompose the Volterra systems into three subsystems with a smaller number of parameters and to estimates the parameters of each subsystem, respectively. The calculation analysis indicates that the proposed algorithm has less computational cost than the recursive least squares algorithm. Finally, the simulation results indicate that the proposed algorithm are effective for identifying Volterra systems.

Keywords Hierarchical identification, nonlinear system, parameter identification, recursive least squares, Volterra system.

Article

Regular Papers

International Journal of Control, Automation and Systems 2022; 20(12): 3940-3950

Published online December 1, 2022 https://doi.org/10.1007/s12555-021-0845-y

Copyright © The International Journal of Control, Automation, and Systems.

Hierarchical Recursive Least Squares Estimation Algorithm for Second-order Volterra Nonlinear Systems

Jian Pan*, Sunde Liu, Jun Shu, and Xiangkui Wan

Hubei University of Technology

Abstract

This paper considers the parameter identification problems of a Volterra nonlinear system. In order to overcome the excessive calculation amount of the Volterra systems, a hierarchical least squares algorithm is proposed through combining the hierarchical identification principle. The key is to decompose the Volterra systems into three subsystems with a smaller number of parameters and to estimates the parameters of each subsystem, respectively. The calculation analysis indicates that the proposed algorithm has less computational cost than the recursive least squares algorithm. Finally, the simulation results indicate that the proposed algorithm are effective for identifying Volterra systems.

Keywords: Hierarchical identification, nonlinear system, parameter identification, recursive least squares, Volterra system.

IJCAS
March 2025

Vol. 23, No. 3, pp. 683~972

Stats or Metrics

Share this article on

  • line

Related articles in IJCAS

IJCAS

eISSN 2005-4092
pISSN 1598-6446