An improved computationally efficient identification method for errors-in-variables models based on v-gap optimisation

ABSTRACT

A v-gap optimisation-based frequency-domain identification method is proposed to estimate a nominal normalised right graph symbol for errors-in-variables models. The proposed method follows the similar identification idea from a previous research work but with improved computational efficiency using interior-point (IP) algorithms. By imposing an inner function constraint instead of frequency point-wise constraints to normalise the graph symbol, the number of involved equality constraints for the v-gap optimisation is related to the nominal model order instead of the data length. As a consequence, the computational complexity of the proposed IP-based identification algorithm is much lower than that of linear matrix inequalities-based algorithms. Due to the fact that the data length is typically much larger than the finite nominal model order, the number of saved equality constraints is close to that of the employed data points. Finally, two numerical simulation examples are given to verify the proposed identification method.

KEYWORDS:

• V-gap optimisation
• computational efficiency
• errors-in-variables models
• interior-point algorithm

Acknowledgments

The author would like to thank the anonymous reviewers for their helpful suggestions and comments to improve the quality of this paper.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported in part by the Tianjin Natural Science Foundation of China [grant number 14JCZDJC36300], [grant number 15JCYBJC19000]; and the Tianjin University of Technology and Education Funded Projects [grant number RC14-48], [grant number RC17-01].