Auxiliary model maximum likelihood least squares-based iterative algorithm for multivariable autoregressive output-error autoregressive moving average systems
Restricted accessResearch articleFirst published online March, 2025
Auxiliary model maximum likelihood least squares-based iterative algorithm for multivariable autoregressive output-error autoregressive moving average systems
Identification of multivariable systems is of great significance to control systems. This paper focuses on the parameter identification problems for multivariable autoregressive output-error autoregressive moving average (M-AROEARMA) systems. On the basis of the decomposition strategy, the M-AROEARMA model is de- composed into multiple subsystem models. By means of the auxiliary model idea, the auxiliary model least squares-based iterative algorithm is derived. For the purpose of achieving highly accurate parameter identification performance under colored noises interference, an auxiliary model maximum likelihood least squares-based iterative algorithm is proposed by utilizing the maximum likelihood principle. The numerical simulation example demonstrates the effectiveness of the proposed algorithms.
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