Challenges in the Identification of Discrete Time Block Oriented Models for Continuous Time Nonlinear Systems**This work was supported in part by the Fund for Scientific Research (FWO-Vlaanderen), by the Flemish Government (Methusalem), the Belgian Government through the Inter university Poles of Attraction (IAP VII) Program, and by the ERC advanced grant SNLSID, under contract 320378.

Schoukens, Joannes; Vaes, Mark; Esfahani, Alireza; Relan, Rishi

doi:10.1016/j.ifacol.2015.12.194

Cited by 3 publications

References 30 publications

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Identification method based on Zadeh filter models

Evdokimov

Tsarev

Yamskikh

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

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Mathematical modeling which provides the description of objects and proper organization of control operations in future is an integral stage in the automation of production. One of the approaches to build a mathematical model of an object is to represent nonlinear systems as combinations of inertial and nonlinear inertialess elements. The models thus obtained are called block-oriented. In this paper, we consider nonlinear dynamic objects represented as the models of the Zadeh filter class. In the process of the method development the identification equations were derived for the case when the test signal is a single sinusoid. Then the case of two sinusoids was considered. Such investigations allowed us to identify the patterns and describe the general case for several test components in the signal. The results of digital modeling using the sum of harmonic signals confirm the feasibility and validity of the proposed approach for identifying nonlinear models of the Zadeh filter class.

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Identification method based on Zadeh filter models

Evdokimov

Tsarev

Yamskikh

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

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Equation Discovery for Nonlinear System Identification

et al. 2020

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Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method described and evaluated in this paper can be used as a nonlinear system identification method for gray-box modeling. It consists of two interlaced parts of modeling that are computer-aided. The first performs computeraided identification of a model structure composed of elements selected from user-specified domain-specific modeling knowledge, while the second part performs parameter estimation.In this paper, recent developments of the equation discovery method called process-based modeling, suited for nonlinear system identification, are elaborated and illustrated on two continuous-time case studies. The first case study illustrates the use of the process-based modeling on synthetic data while the second case-study evaluates on measured data for a standard system-identification benchmark. The experimental results clearly demonstrate the ability of process-based modeling to reconstruct both model structure and parameters from measured data. Index Terms-Software for system identification, Gray-box modeling, Nonlinear system identification, Equation discovery, Process-based modeling, Knowledge-based identification, Machine learningAll authors are affiliated with the Jožef Stefan Institute,

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Identification and control of Sandwich system with backlash

Ren

Wang

et al. 2017

2017 36th Chinese Control Conference (CCC)

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Cited by 3 publications

References 30 publications

Identification method based on Zadeh filter models

Identification method based on Zadeh filter models

Equation Discovery for Nonlinear System Identification

Identification and control of Sandwich system with backlash

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