MIMO system identification using common denominator and numerators with known degrees

Abstract

Summary In system identification, prior knowledge about the model structure may be available. However, imposing this structure on the identified model may be nontrivial. A new discrete‐time linear time‐invariant identification method is presented in the article that imposes prior knowledge of the degree of the common denominator of the system's transfer function matrix and the degrees of the numerators. First, a method is outlined for the solution in case of exact data. Then, this method is extended for noisy data in the output error setting. An initial estimate obtained by a subspace method is improved by a structured low‐rank approximation method. The performance of the method imposing the structure is compared on simulated data with the performance of classical identification methods that do not impose the structure.

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

“…Compared with scalar systems, MIMO systems have higher orders and more parameters, which brings more challenges to establish the mathematical model of MIMO systems. For the identification of MIMO systems, many scholars have studied the relevant identification algorithms 18 . For output‐error linear MIMO models, Formentin et al developed a novel theoretical framework for the control‐oriented identification based on a Bayesian perspective on modeling and derived a Bayesian robust control design approach 19 .…”

“…For the identification of MIMO systems, many scholars have studied the relevant identification algorithms. 18 For output-error linear MIMO models, Formentin et al developed a novel theoretical framework for the control-oriented identification based on a Bayesian perspective on modeling and derived a Bayesian robust control design approach. 19 Cerone et al studied the structured discrete-time nonlinear systems and proposed a single-stage set-membership identification algorithm to solve the problem in the context of the set-membership errors-in-variables identification.…”

Highly computational efficiency identification algorithm for multiple-input multiple-output systems

“…Compared with scalar systems, MIMO systems have higher orders and more parameters, which brings more challenges to establish the mathematical model of MIMO systems. For the identification of 2 H.M. XING, F. DING, F. PAN MIMO systems, many scholars have studied the relevant identification algorithms [15][16][17]. For output-error linear MIMO models, Formentin et al developed a novel theoretical framework for the control-oriented identification based on a Bayesian perspective on modeling and derived a Bayesian robust control design approach [18].…”