MIMO system identification: state-space and subspace approximations versus transfer function and instrumental variables

2000 ◽  
Vol 48 (11) ◽  
pp. 3087-3099 ◽  
Author(s):  
P. Stoica ◽  
M. Jansson
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
State Space ◽  
Instrumental Variables ◽  
Mimo System

scholarly journals The Transfer Function of the SARS-CoV-2 Spread in Different Countries with Stability, Observability, and Controllability Analysis

2021 ◽  
Author(s):  
Jasmina Lozanović Šajić ◽  
Sonja Langthaler ◽  
Sara Stoppacher ◽  
Christian Baumgartner
Keyword(s):  
Differential Equation ◽  
System Theory ◽  
Control System ◽  
System Identification ◽  
Transfer Function ◽  
State Space ◽  
Separate System ◽  
The Stability ◽  
Controllability And Observability

Abstract This paper presents the determination of the transfer function of the spreading pandemic caused by SARS-CoV-2 in different countries. The methodology of system identification, well known in control system theory, based on the number of infected was used. Appropriate hypotheses have been adopted to determine the transfer function of the system. Each country is viewed as a separate system, and comparisons of determined systems are given. The systems are also presented in the state space, the stability of the systems is analysed, and the matrices of controllability and observability are determined. After analysis, it is shown that the spread of the SARS-CoV-2, for each country, can be described with the same order of transfer function and differential equation.

Data-Driven Subspace Predictive Control for a MIMO System

2013 ◽  
Vol 594-595 ◽  
pp. 1078-1082
Author(s):  
Irma Wani Jamaludin ◽  
Norhaliza Abdul Wahab ◽  
Muhammad Sani Gaya
Keyword(s):  
System Identification ◽  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Controller Design ◽  
Mimo System ◽  
Waste Water Treatment ◽  
Space Representation ◽  
Linear Predictor ◽  
State Space Representation

Subspace-based Model Predictive Control (SMPC) is a combination of a result in subspace system identification with Model Predictive Control (MPC) method. Particularly, it uses the subspace linear predictor equation to predict the future value of the system in the MPC implementation, instead of the usual state-space representation. The recursive subspace identification which updates the estimation of the extended observability matrix online is presented here for a Multi Input-Multi Output (MIMO) system specifically for a nonlinear Biological Waste Water Treatment Process. Givens rotation is applied for recursive updating of QR decomposition of a matrix in this SMPC. In SMPC, the need to have an explicit state-space representation of the system is abolished, resulting in a control algorithm that performs system identification and controller design in a single simultaneous step. Additionally, SMPC algorithm will inherit the numerical robustness typical of subspace-based methods thus giving us an easily deployable control implementation in adaptive framework.

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