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
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.
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.
Jian Pan*, Sunde Liu, Jun Shu, and Xiangkui Wan
Hubei University of Technology
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.
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