Highly‐computational hierarchical iterative identification methods for multiple‐input multiple‐output systems by using the auxiliary models

Abstract

The identification of multiple‐input multiple‐output (MIMO) systems is an important part of designing complex control systems. This article studies an auxiliary model least squares iterative (AM‐LSI) algorithm for MIMO systems. With the expansion of the system scale and limitations of the computer resources, there is an urgent need for an identification algorithm that provides higher computational efficiency. To address this issue, this article further derives a hierarchical identification model and proposes a new auxiliary model hierarchical least squares iterative (AM‐HLSI) algorithm for MIMO systems by applying the hierarchical identification principle. Through the analysis of the computational efficiency, the AM‐HLSI algorithm has higher computational efficiency than the AM‐LSI algorithm. Additionally, the feasibility of the AM‐LSI and AM‐HLSI algorithms is validated by a simulation example.

Decomposition‐based maximum likelihood gradient iterative algorithm for multivariate systems with colored noise

“…The proposed D-M-ML-GI algorithm has excellent performance in identifying MIMO systems and can be applied to complex linear and nonlinear multivariable systems. The proposed iterative estimation algorithm in this paper for multivariable stochastic systems joints strategies [122][123][124][125][126][127][128][129][130][131][132][133] to explore new maximum likelihood parameter estimation methods of other linear and nonlinear stochastic systems with ARMA noises [134][135][136][137][138][139][140][141][142][143] and can be applied to other areas [144][145][146][147][148][149][150][151][152][153] such as industrial process systems and paper-making systems.…”

“…System modeling and model parameter estimation are prerequisites for control systems analysis and design. [122][123][124][125][126] After years of development, many parameter estimation methods have been proposed for linear systems and nonlinear systems, [127][128][129][130][131] such as the least squares methods, the iterative methods, the maximum likelihood methods, [132][133][134] and the robust identification methods, 135,136 and so forth. Due to the fact that multivariable systems can better describe the characteristics of actual industrial process objects than scalar systems, research on parameter estimation methods for multivariable systems has received widespread attention in recent years, and a series of research results have been achieved.…”

Hierarchical recursive least squares parameter estimation methods for multiple‐input multiple‐output systems by using the auxiliary models

“…Recently, the previous work discusses the highly-computational hierarchical iterative identification methods for multiple-input multiple-output systems, 28,37 this article investigates the hierarchical recursive least squares identification methods for the MIMO system described by the following output-error model,…”