Time Domain System Identification for Small-Scale Unmanned Helicopters Using Fuzzy Models

2011 ◽  
pp. 153-159
Author(s):  
Ioannis A. Raptis ◽  
Kimon P. Valavanis
Keyword(s):  
System Identification ◽  
Time Domain ◽  
Small Scale ◽  
Fuzzy Models

scholarly journals Investigating Data-Driven Systems as Digital Twins: Numerical Behavior of Ho–Kalman Method for Order Estimation

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 431
Author(s):  
Alexios Papacharalampopoulos
Keyword(s):  
System Identification ◽  
Time Domain ◽  
Data Driven ◽  
Driven Systems ◽  
Control Techniques ◽  
Digital Twin ◽  
Order Estimation ◽  
Digital Twins ◽  
Initial Stage ◽  
System Order

System identification has been a major advancement in the evolution of engineering. As it is by default the first step towards a significant set of adaptive control techniques, it is imperative for engineers to apply it in order to practice control. Given that system identification could be useful in creating a digital twin, this work focuses on the initial stage of the procedure by discussing simplistic system order identification. Through specific numerical examples, this study constitutes an investigation on the most “natural” method for estimating the order from responses in a convenient and seamless way in time-domain. The method itself, originally proposed by Ho and Kalman and utilizing linear algebra, is an intuitive tool retrieving information out of the data themselves. Finally, with the help of the limitations of the methods, the potential future outlook is discussed, under the prism of forming a digital twin.

Servo System Identification Using Relay Feedback: A Time-Domain Approach

2012 ◽  
Vol 134 (6) ◽  
Author(s):  
Jia Liu ◽  
Jianhua Wu ◽  
Zhenhua Xiong ◽  
Xiangyang Zhu
Keyword(s):  
System Identification ◽  
Dynamic Characteristics ◽  
Time Domain ◽  
Oscillation Amplitude ◽  
Servo System ◽  
Time Domain Method ◽  
Relay Feedback ◽  
Servo Systems ◽  
Domain Method ◽  
The Time Domain

In servo systems, the dynamic characteristics may not only differ between axes but may also vary with moving directions for a single axis. The direction dependent characteristics would result in additional tracking or positioning error and degrade the performance of the system. In this paper, relay feedback tests are successfully applied to identify the dynamic characteristics in servo systems. A time-domain method is used to analyze the relay feedback other than the conventional describing function (DF) method. The time-domain method utilizes the same oscillation parameters (oscillation amplitude and half period) as the DF method for system identification. However, the time-domain method takes several advantages: First, the direction dependent characteristics of the system can be properly revealed; second, no approximation is made in this method, so that the exact expressions of the amplitudes and the periods of the limit cycles under relay feedback can be derived. A feedforward compensator is then designed using the estimated values of the system parameters. Simulation results show that the identification results through the time-domain method are more accurate than the DF method and are more robust under different relay parameters. Real time experiments show that the feedforward compensator designed by the proposed method compensates disturbances related to the direction and hence improves the tracking and positioning performance of the servo system.

Optimal Accelerometer Locations for Structural Health Monitoring Using Time-Domain System Identification

2006 ◽  
Vol 321-323 ◽  
pp. 273-277 ◽  
Author(s):  
Soon Jung Kwon ◽  
Hae Sung Lee ◽  
Soo Bong Shin
Keyword(s):  
System Identification ◽  
Structural Health Monitoring ◽  
Fisher Information ◽  
Health Monitoring ◽  
Time Domain ◽  
Fisher Information Matrix ◽  
Structural Parameters ◽  
Hessian Matrix ◽  
Information Matrix ◽  
Structural Health

The paper presents two algorithms for determining optimal accelerometer locations for structural health monitoring when structural condition is assessed by a system identification scheme in time-domain. The accelerometer locations are determined by ranking the components of an effective independent distribution vector computed from a Fisher information matrix. One of the proposed algorithms formulates a Fisher information matrix by multiplying acceleration matrix with its transpose and the other as a Gauss-Newton Hessian matrix composed of acceleration sensitivities with respect to structural parameters. Since the structural parameters cannot be known exactly in an actual application, a statistical approach is proposed by setting an error bound between the actual and the baseline values. To examine the algorithm, simulation studies have been carried out on a two-span planar truss. The results using locations selected by the two algorithms were compared.

System Identification of Jack-Up Platform by Spectral Analysis

2010 ◽  
Author(s):  
X. M. Wang ◽  
C. G. Koh ◽  
T. N. Thanh ◽  
J. Zhang
Keyword(s):  
Spectral Analysis ◽  
System Identification ◽  
Time Domain ◽  
Time History ◽  
Offshore Structures ◽  
Wave Load ◽  
History Of ◽  
Structural Responses ◽  
Jack Up ◽  
Numerical Strategy

For the purpose of structural health monitoring (SHM), it is beneficial to develop a robust and accurate numerical strategy so as to identify key parameters of offshore structures. In this regard, it is difficult to use time-domain methods as the time history of wave load is not available unless output-only methods can be developed. Alternatively, spectral analysis widely used in offshore engineering to predict structural responses due to random wave conditions can be used. Thus the power spectral density (PSD) of structural response may be more appropriate than time history of structural responses in defining the objective (fitness) function for system identification of offshore structures. By minimizing PSD differences between measurements and simulations, the proposed numerical strategy is completely carried out in frequency domain, which can avoid inherent problems rising from random phase angles and unknown initial conditions in time domain. A jack-up platform is studied in the numerical study. A search space reduction method (SSRM) incorporating the use of genetic algorithms (GA) as well as a substructure approach are adopted to improve the accuracy and efficiency of identification. As a result, the stiffness parameters of jack-up legs can be well identified even under fairly noisy conditions.

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