ID:
publications-2370
Type:
Peer reviewed articles
Year:
2019
Authors:
Witold Byrski, Michał Drapała, Jȩdrzej Byrski
Title:
An Adaptive Identification Method Based on the Modulating Functions Technique and Exact State Observers for Modeling and Simulation of a Nonlinear Miso Glass Melting Process
Venue/Journal:
International Journal of Applied Mathematics and Computer Science
DOI:
10.2478/amcs-2019-0055
Research type:
AI & Machine Learning
Water System:
Irrigation Systems
Technical Focus:
Abstract:
Abstract The paper presents new concepts of the identification method based on modulating functions and exact state observers with its application for identification of a real continuous-time industrial process. The method enables transformation of a system of differential equations into an algebraic one with the same parameters. Then, these parameters can be estimated using the least-squares approach. The main problem is the nonlinearity of the MISO process and its noticeable transport delays. It requires specific modifications to be introduced into the basic identification algorithm. The main goal of the method is to obtain on-line a temporary linear model of the process around the selected operating point, because fast methods for tuning PID controller parameters for such a model are well known. Hence, a special adaptive identification approach with a moving window is proposed, which involves using on-line registered input and output process data. An optimal identification method for a MISO model assuming decomposition to many inner SISO systems is presented. Additionally, a special version of the modulating functions method, in which both model parameters and unknown delays are identified, is tested on real data sets collected from a glass melting installation.
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