scholarly journals Nonlinear System Identification using Uncoupled State Multi-model Approach: Application to the PCB Soldering System

2020 ◽  
Vol 10 (1) ◽  
pp. 5221-5227
Author(s):  
S. Khouni ◽  
K. E. Hemsas
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Printed Circuit Boards ◽  
Nonlinear System Identification ◽  
Stability Control ◽  
Complex Nature ◽  
Circuit Boards ◽  
Systems Methodology ◽  
Model Approach

Multi-model approach is an adapted tool of modeling nonlinear systems. The underlying idea is to simplify the complex nature of the system to be studied by decomposing it into simple (linear) sub-systems, in order to simplify the study (stability, control law, surveillance, etc.). This technique allows us to extend the application of linear systems methodology to nonlinear systems. This paper presents nonlinear system identification using an uncoupled state multi-model applied to a Printed Circuit Boards (PCB) soldering system. Precision, simplicity, and fidelity of the obtained results show the effectiveness of the used algorithm to identify, model, and write down as simple sub-systems, a complex black box system.

scholarly journals A Multiple-Model Approach for Synchronous Generator Nonlinear System Identification

2012 ◽  
Vol 63 (4) ◽  
pp. 249-254 ◽  
Author(s):  
Seyed Ahmadi ◽  
Mehdi Karrari
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Nonlinear Behavior ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Complexity ◽  
Multiple Model ◽  
Local Models ◽  
Model Approach

A Multiple-Model Approach for Synchronous Generator Nonlinear System Identification In this paper, a multiple model approach is proposed for the identification of synchronous generators. In the literature, the same structure often is used for all local models. Therefore, to obtain a precise model for the operating condition of the synchronous generator with severely nonlinear behavior, many local models are required. The proposed method determines the complexity of local models based on complexity of behavior of the synchronous generator at different operating conditions. There are two choices for increasing model precision at each iteration of the proposed method: (i) increasing the number of local models in one region, or (ii) increasing local model complexity in the same region. The proposed method has been tested on experimental data collected on a 3 kVA micro-machine. In the study, the field voltage is considered as the input and the active output power and the terminal voltage are considered as the outputs of the synchronous generator. The proposed method provides a more precise model with fewer parameters compared to some well known methods such as LOLIMOT and global polynomial models.

Conditioned and Orthogonalised Reverse Path Nonlinear Methods on Multi-Degree-of-Freedom System

2015 ◽  
Vol 752-753 ◽  
pp. 558-563
Author(s):  
H.M.Y. Norfazrina ◽  
P. Muhamad ◽  
B.A. Aminudin ◽  
M.R. Raihan ◽  
A.W. Azella ◽  
...  
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Nonlinear System Identification ◽  
Nonlinear Problems ◽  
Degree Of Freedom ◽  
Engineering Structures ◽  
Nonlinear Dynamic Response ◽  
Practical Engineering ◽  
Reverse Path

Practical engineering structures commonly display nonlinear dynamic response when damage is present in the system. Hence, the studies on nonlinear system identification have increased within these past few years. Current study is aimed on the structural identification of nonlinear systems based on the extraction of underlying linear frequency response function (FRF). The methods chosen to obtain the FRF are the Conditioned Reverse Path (CRP) and the Orthogonalised Reverse Path (ORP) method. The well-known frequency-domain CRP method has been recognised for its ability in solving nonlinear problems; detecting and quantifying nonlinearities in structures. In contrary, the ORP is a new algorithm developed in time-domain which gives simpler formulation for describing the underlying linear dynamics of nonlinear systems. Results show that the performance of the new ORP algorithm in handling nonlinearities is as good as the CRP method. The ability of ORP method has become the aim of the current study to assess the robustness of both algorithms towards nonlinear system identification of structures with multi-degree-of-freedom (MDOF) system.

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