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Parameter estimation of MIMO two-dimensional ARMAX model based on IGLS method

Hayati Zohreh, Shafieirad Mohsen, Zamani Iman, Amiri Mehra Amir Hossein, Abbasi Zohreh

Control and Cybernetics

2021

Vol. 50, No. 3

303--322

This paper presents an iterative method for the unbiased identification of linearMultiple-InputMultiple-Output (MIMO) discrete two-dimensional (2D) systems. The system discussed here has Auto-Regressive Moving-Average model with exogenous inputs (ARMAX model). The proposed algorithm functions on the basis of the traditional Iterative Generalized Least Squares (IGLS) method. In summary, this paper proposes a two-dimensional Multiple-Input Multiple-Output Iterative Generalized Least Squares (2DMIGLS) algorithm to estimate the unknown parameters of the ARMAX model. Finally, simulation results show the efficiency and accuracy of the presented algorithm in estimating the unknown parameters of the model in the presence of colored noise.

An iterative identification based on the Youla-Kucera parameterisation without model reduction

Kozka S., Mikles J., Fikar M.

Archives of Control Sciences

2003

Vol. 13, no. 1

19-31

The aim of this paper is to present an iterative identification of the plant in the presence of feedback using input-output data, based on the Youla-Kucera parameterisation. When a reduced complexity model is identified then the controller is designed. here the identified model is just a vehicle for the computation of a controller. The proposed iterative algorithm contains suitably selected filters and ensures that the model reduction of the identified model is not necessary as in the standard approach. This iterative approach simplifies the identification task of the Youla-Kucera parameter as only its numerator has to be estimated. an experiment with a continuous stirred tank reactor (CSTR), with represents a non-linear single input -single output (SISO)system, illustrate this feature. LQ controller design is used for stabilisation.