scholarly journals Laboratory Calibration of D-dot Sensor Based on System Identification Method

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3255 ◽  
Author(s):  
Ke Wang ◽  
Yantao Duan ◽  
Lihua Shi ◽  
Shi Qiu
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Electric Fields ◽  
Linear Time ◽  
Low Frequency ◽  
Calibration Method ◽  
Sensitivity Coefficient ◽  
Corner Frequency ◽  
Linear Time Invariant ◽  
The Time Domain

D-dot sensors can realize the non-contact measurement of transient electric fields, which is widely applied to electromagnetic pulse (EMP) measurements with characteristics of the wide frequency band, high linearity, and good stability. In order to achieve accurate calibration of D-dot sensors in the laboratory environment, this paper proposed a new calibration method based on system identification. Firstly, the D-dot sensor can be considered as a linear time-invariant (LTI) system under corner frequency, thus its frequency response can be characterized by the transfer function of a discrete output error (OE) model. Secondly, based on the partial linear regression of the transfer function curve, the sensitivity coefficient of the D-dot sensor is obtained. By increasing the influence weight of low-frequency components, this proposed method has better calibration performance when the waveform is distorted in the time domain, and can artificially adapt to the operating frequency range of the sensor at the same time.

Estimating the forcing function in a mechanical system by an inverse calibration method

2021 ◽  
pp. 107754632110310
Author(s):  
Chapel Rice ◽  
Jay I Frankel
Keyword(s):  
Experimental Data ◽  
Transfer Function ◽  
Linear Time ◽  
Regularization Parameter ◽  
Calibration Method ◽  
Phase Plane Analysis ◽  
Constant Property ◽  
Vehicle Suspensions ◽  
Linear Time Invariant ◽  
Forcing Function

This article proposes and demonstrates a calibration-based integral formulation for resolving the forcing function in a mass–spring–damper system, given either displacement or acceleration data. The proposed method is novel in the context of vibrations, being thoroughly studied in the field of heat transfer. The approach can be expanded and generalized further to multi-variable systems associated with machine parts, vehicle suspensions, translational and rotational systems, gear systems, etc. when mathematically described by a system of constant property, linear, time-invariant ordinary differential equations. The analytic approach and subsequent numerical reconstruction of the forcing function is based on resolving a parameter-free inverse formulation for the equation(s) of motion. The calibration approach is formulated in the frequency domain and takes advantage of several observations produced by the dimensionality reduction leading to an algebratized system involving an input–output relationship and a transfer function possessing all the system parameters. The transfer function is eliminated in lieu of experimental data, from a calibration effort, thus leading to a reduction of systematic errors. These parameter-free, reduced systematic error aspects are the distinct and novel advantages of the proposed method. A first-kind Volterra integral equation is formed containing only the unknown forcing function and experimental data. As with all ill-posed problems, regularization must be introduced for system stabilization. A future-time technique is instituted for forming a family of predictions based on the chosen regularization parameter. The optimal regularization parameter is estimated using a combination of phase–plane analysis and cross-correlation principles. Finally, a numerical simulation is performed verifying the proposed approach.

scholarly journals A Unified Frequency Domain Model to Study the Effect of Demyelination on Axonal Conduction

2016 ◽  
Vol 7 ◽  
pp. BECB.S38554 ◽  
Author(s):  
Saurabh Chaubey ◽  
Shikha J. Goodwin
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Frequency Domain ◽  
Linear Time ◽  
Signal Propagation ◽  
Nerve Fibers ◽  
Domain Model ◽  
Linear Time Invariant ◽  
Cable Properties ◽  
Identification Approach

Multiple sclerosis is a disease caused by demyelination of nerve fibers. In order to determine the loss of signal with the percentage of demyelination, we need to develop models that can simulate this effect. Existing time-based models does not provide a method to determine the influences of demyelination based on simulation results. Our goal is to develop a system identification approach to generate a transfer function in the frequency domain. The idea is to create a unified modeling approach for neural action potential propagation along the length of an axon containing number of Nodes of Ranvier (N). A system identification approach has been used to identify a transfer function of the classical Hodgkin-Huxley equations for membrane voltage potential. Using this approach, we model cable properties and signal propagation along the length of the axon with N node myelination. MATLAB/ Simulink platform is used to analyze an N node-myelinated neuronal axon. The ability to transfer function in the frequency domain will help reduce effort and will give a much more realistic feel when compared to the classical time-based approach. Once a transfer function is identified, the conduction as a cascade of each linear time invariant system-based transfer function can be modeled. Using this approach, future studies can model the loss of myelin in various parts of nervous system.

Identification of Multivariable Nonlinear Dynamic System Based on PID Neural Network

2015 ◽  
Vol 719-720 ◽  
pp. 475-481
Author(s):  
Hua Shu ◽  
Huai Lin Shu
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Dynamic System ◽  
Nonlinear Dynamic ◽  
Linear Time ◽  
Nonlinear Dynamic System ◽  
Identification Methods ◽  
Linear Time Invariant ◽  
Linear Time Invariant Systems ◽  
Pid Neural Network

System identification is the basis for control system design. For linear time-invariant systems have a variety of identification methods, identification methods for nonlinear dynamic system is still in the exploratory stage. Nonlinear identification method based on neural network is a simple and effective general method that does not require too much priori experience about the system to be identified. Through training and learning, the network weights are corrected to achieve the purpose of system identification. The paper is about the identification of multivariable nonlinear dynamic system based on PID neural network. The structure and algorithm of PID neural network are introduced and the properties and characteristics are analyzed. The system identification is completed and the results are fast convergence.

Floquet Experimental Modal Analysis for System Identification of Linear Time-Periodic Systems

2007 ◽  
Author(s):  
Matthew S. Allen
Keyword(s):  
System Identification ◽  
Linear Time ◽  
Time Varying ◽  
Response Model ◽  
State Matrix ◽  
Linear Time Invariant ◽  
Response Data ◽  
Time Varying Systems ◽  
Time Invariant ◽  
Time Periodic

A variety of systems can be faithfully modeled as linear with coefficients that vary periodically with time or Linear Time-Periodic (LTP). Examples include anisotropic rotorbearing systems, wind turbines, satellite systems, etc… A number of powerful techniques have been presented in the past few decades, so that one might expect to model or control an LTP system with relative ease compared to time varying systems in general. However, few, if any, methods exist for experimentally characterizing LTP systems. This work seeks to produce a set of tools that can be used to characterize LTP systems completely through experiment. While such an approach is commonplace for LTI systems, all current methods for time varying systems require either that the system parameters vary slowly with time or else simply identify a few parameters of a pre-defined model to response data. A previous work presented two methods by which system identification techniques for linear time invariant (LTI) systems could be used to identify a response model for an LTP system from free response data. One of these allows the system’s model order to be determined exactly as if the system were linear time-invariant. This work presents a means whereby the response model identified in the previous work can be used to generate the full state transition matrix and the underlying time varying state matrix from an identified LTP response model and illustrates the entire system-identification process using simulated response data for a Jeffcott rotor in anisotropic bearings.

Definition of a Complex Domain Adjoint for Use in Optimal Control Problems

1978 ◽  
Vol 11 (1) ◽  
pp. 33-35
Author(s):  
M. J. Grimble
Keyword(s):  
Linear Time ◽  
Adjoint Operator ◽  
Adjoint System ◽  
Simple Relationship ◽  
Complex Frequency ◽  
Simple Method ◽  
Linear Time Invariant ◽  
Definition Of ◽  
The Time Domain ◽  
Linear Time Invariant System

A complex frequency version of the time domain adjoint operator for a linear time-invariant system is obtained. A very simple relationship is shown to exist between this operator and the system transfer function matrix. A simple method of simulating the adjoint system is described.

System Identification of Force of a Silver Coated Twisted and Coiled Polymer Muscle

2017 ◽  
Author(s):  
Mohsen Jafarzadeh ◽  
Lianjun Wu ◽  
Yonas Tadesse
Keyword(s):  
System Identification ◽  
Differential Equations ◽  
Linear Time ◽  
Electrical Power ◽  
Artificial Muscle ◽  
Variable Stiffness ◽  
Extensive Study ◽  
Fast Method ◽  
Linear Time Invariant ◽  
State Space System

The demand of using artificial muscle similar to the human muscle is significantly increased during past decades. Recently, silver-plated Twisted and Coiled Polymer (TCP) muscle was employed in many research projects. A first order differential equations (1st ODE) was used to predict the force of this muscle, assuming that the TCP muscle acts similar to a mechanical spring that has variable stiffness depending on the electrical power supplied. Thus, extensive study should be performed on different types of TCP muscles to reach a conclusion. In this paper, a black box system identification method is used to examine the behavior of TCP muscles under different input conditions. Different order differential equations are compared with experimental results. Prediction error method (PEM) is used for estimation of the force of silver-plated TCP muscle with several linear time invariant (LTI) discrete time state space system. In addition, we suggest a fast method (rule of thumb) to model a TCP muscle. Moreover, two key parameters have been introduced to compare the quality of the TCP muscle from force perspective.

The Dynamic Calibration Uncertainty of Fast Response Pressure Probes

2020 ◽  
Author(s):  
Elissavet Boufidi ◽  
Fabrizio Fontaneto
Keyword(s):  
Transfer Function ◽  
Linear Time ◽  
Fast Response ◽  
Dynamic Calibration ◽  
Shock Tubes ◽  
Linear Time Invariant ◽  
Uncertainty Sources ◽  
Pressure Step ◽  
Post Shock ◽  
Response Pressure

Abstract In this paper, error sources affecting the dynamic calibration of fast response pressure probes in shock tubes are examined. In particular, the sensors uncertainty, the uncertainty in the rising point of the pressure step and the nonideality of the step are treated. The latter refers to the presence of pressure oscillations past the shock front, which are particularly important in the case of low-pressure shock tubes, typically used for the calibration of pressure probes for turbomachinery applications. The nonideality effect is investigated using a Linear Time Invariant (LTI) second order model for the transfer function of the probe’s line-cavity system and an existing analytical model for the post-shock oscillations. The effect of these uncertainty sources to the experimentally determined transfer function of a fast response probe calibrated in the von Karman Institute (VKI) shock tube are finally presented.

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