Kavli Affiliate: Ran Wang
| First 5 Authors: Mohamed Naveed Gul Mohamed, Raman Goyal, Suman Chakravorty, Ran Wang,
| Summary:
This paper considers the problem of system identification for linear time
varying systems. We propose a new system realization approach that uses an
"information-state" as the state vector, where the "information-state" is
composed of a finite number of past inputs and outputs. The system
identification algorithm uses input-output data to fit an autoregressive moving
average model (ARMA) to represent the current output in terms of finite past
inputs and outputs. This information-state-based approach allows us to directly
realize a state-space model using the estimated time varying ARMA paramters
linear time varying (LTV) systems. The paper develops the theoretical
foundation for using ARMA parameters-based system representation using only the
concept of linear observability, details the reasoning for exact output
modeling using only the finite history, and shows that there is no need to
separate the free and the forced response for identification. The paper also
discusses the implications of using the information-state system for optimal
output feedback control and shows that the solution obtained using a suitably
posed information state problem is optimal for the original problem. The
proposed approach is tested on various different systems, and the performance
is compared with state-of-the-art LTV system identification techniques.
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