scholarly journals Linear and nonlinear modeling of self-excited acoustic oscillations in a T-shaped thermoacoustic engine

AIP Advances ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 085120
Author(s):  
Geng Chen ◽  
Haitian Hao ◽  
Aidong Deng
Keyword(s):  
Nonlinear Modeling ◽  
Acoustic Oscillations ◽  
Thermoacoustic Engine ◽  
Linear And Nonlinear

Miscible Displacement of Zinc in Soil Columns: Linear and Nonlinear Modeling

2013 ◽  
Vol 77 (2) ◽  
pp. 391-402 ◽  
Author(s):  
H. M. Selim ◽  
Tamer A. Elbana ◽  
Keli Zhao ◽  
Jianming Xu ◽  
Eric L. Fergusson
Keyword(s):  
Nonlinear Modeling ◽  
Miscible Displacement ◽  
Soil Columns ◽  
Linear And Nonlinear

LINEAR AND NONLINEAR ARX MODEL FOR INTELLIGENT PNEUMATIC ACTUATOR SYSTEMS

2016 ◽  
Vol 78 (6) ◽  
Author(s):  
Siti Fatimah Sulaiman ◽  
M. F. Rahmat ◽  
A. A. M. Faudzi ◽  
Khairuddin Osman
Keyword(s):  
Dynamic Behavior ◽  
Systems Engineering ◽  
Position Control ◽  
System Modeling ◽  
Nonlinear Modeling ◽  
Pneumatic Actuator ◽  
Arx Model ◽  
Control Systems Engineering ◽  
Nonlinear Mathematical Models ◽  
Linear And Nonlinear

System modeling in describing the dynamic behavior of the system is very important and can be considered as a challenging problem in control systems engineering. This article presents the linear and nonlinear approaches using AutoRegressive with Exogenous Input (ARX) model structure for the modeling of position control of an Intelligent Pneumatic Actuator (IPA) system. The input and output data of the system were obtained from real-time experiment conducted while the linear and nonlinear mathematical models of the system were obtained using system identification (SI) technique. Best fit and Akaike’s criteria were used to validate the models. The results based on simulation reveals that nonlinear ARX (NARX) had the best performance for the modeling of position control of IPA system. The results show that nonlinear modeling is an effective way of analyzing and describing the dynamic behavior and characteristics of IPA system. This approach is also expected to be able to be applied to other systems. A future study exploring the execution of other model structures in demonstrating the position control of IPA system would be exceptionally intriguing.

scholarly journals Nonlinear Modeling of Azimuth Error for 2D Car Navigation Using Parallel Cascade Identification Augmented with Kalman Filtering

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Umar Iqbal ◽  
Jacques Georgy ◽  
Michael J. Korenberg ◽  
Aboelmagd Noureldin
Keyword(s):  
Kalman Filtering ◽  
Urban Areas ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Nonlinear Modeling ◽  
Land Vehicle ◽  
Bias Drift ◽  
Parallel Cascade Identification ◽  
Linear And Nonlinear ◽  
Car Navigation

Present land vehicle navigation relies mostly on the Global Positioning System (GPS) that may be interrupted or deteriorated in urban areas. In order to obtain continuous positioning services in all environments, GPS can be integrated with inertial sensors and vehicle odometer using Kalman filtering (KF). For car navigation, low-cost positioning solutions based on MEMS-based inertial sensors are utilized. To further reduce the cost, a reduced inertial sensor system (RISS) consisting of only one gyroscope and speed measurement (obtained from the car odometer) is integrated with GPS. The MEMS-based gyroscope measurement deteriorates over time due to different errors like the bias drift. These errors may lead to large azimuth errors and mitigating the azimuth errors requires robust modeling of both linear and nonlinear effects. Therefore, this paper presents a solution based on Parallel Cascade Identification (PCI) module that models the azimuth errors and is augmented to KF. The proposed augmented KF-PCI method can handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear and residual parts of the azimuth errors are modeled by PCI. The performance of this method is examined using road test experiments in a land vehicle.

Mapping dependencies of BOLD signal change to end-tidal CO2: linear and nonlinear modeling, and effect of physiological noise correction

2021 ◽  
pp. 109317
Author(s):  
Simone Cauzzo ◽  
Alejandro L. Callara ◽  
Maria Sole Morelli ◽  
Valentina Hartwig ◽  
Fabrizio Esposito ◽  
...  
Keyword(s):  
Nonlinear Modeling ◽  
Bold Signal ◽  
Physiological Noise ◽  
Signal Change ◽  
Linear And Nonlinear ◽  
Noise Correction ◽  
End Tidal ◽  
End Tidal Co2
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