An Improved Autoregressive Spectral Estimation Method Using the Kalman Filter System Identification Technique

1993 ◽  
Author(s):  
Chung-Wen Chen ◽  
Gordon Lee ◽  
Jer-Nan Juang
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Spectral Estimation ◽  
Estimation Method ◽  
Filter System ◽  
Identification Technique

scholarly journals Field Evaluation of the System Identification Approach for Tension Estimation of External Tendons

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Myung-Hyun Noh ◽  
Taek-Ryong Seong ◽  
Kyu-Sik Park
Keyword(s):  
System Identification ◽  
Estimation Method ◽  
Field Evaluation ◽  
Element Model ◽  
Large Error ◽  
Field Application ◽  
Linear Regression Method ◽  
External Tendons ◽  
Identification Technique ◽  
Identification Approach

Various types of external tendons are considered to verify the applicability of tension estimation method based on the finite element model with system identification technique. The proposed method is applied to estimate the tension of benchmark numerical example, model structure, and field structure. The numerical and experimental results show that the existing methods such as taut string theory and linear regression method show large error in the estimated tension when the condition of external tendon is different with the basic assumption used during the derivation of relationship between tension and natural frequency. However, the proposed method gives reasonable results for all of the considered external tendons in this study. Furthermore, the proposed method can evaluate the accuracy of estimated tension indirectly by comparing the measured and calculated natural frequencies. Therefore, the proposed method can be effectively used for field application of various types of external tendons.

An Annular Pulsed Detonation Combustor Mockup: System Identification and Misfiring Detection

2015 ◽  
Vol 138 (4) ◽  
Author(s):  
Sascha Wolff ◽  
Rudibert King
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Initial Conditions ◽  
Environmental Parameters ◽  
Modal Identification ◽  
Experimental Setup ◽  
Lower Performance ◽  
Model Based ◽  
Pulsed Detonation ◽  
Identification Technique

An annular pulsed detonation combustor (PDC) basically consists of a number of detonation tubes which are firing in a predetermined sequence into a common downstream annular plenum. Fluctuating initial conditions and fluctuating environmental parameters strongly affect the detonation. Operating such a setup without misfiring is delicate. Misfiring of individual combustion tubes will significantly lower performance or even stop the engine. Hence, an operation of such an engine requires a misfiring detection. Here, a model-based approach is used which exploits the innovation sequence calculated by a Kalman filter. The model necessary for the Kalman filter is determined based on a modal identification technique. A surrogate, nonreacting experimental setup is considered in order to develop and test these methods.

An Annular Pulsed Detonation Combustor Mockup: System Identification and Misfiring Detection

2015 ◽  
Author(s):  
Sascha Wolff ◽  
Rudibert King
Keyword(s):  
Kalman Filter ◽  
System Identification ◽  
Initial Conditions ◽  
Environmental Parameters ◽  
Modal Identification ◽  
Lower Performance ◽  
Model Based ◽  
Pulsed Detonation ◽  
Identification Technique ◽  
Set Up

An annular pulsed detonation combustor basically consists of a number of detonation tubes which are firing in a predetermined sequence into a common downstream annular plenum. Fluctuating initial conditions and fluctuating environmental parameters strongly affect the detonation. Operating such a set-up without misfiring is delicate. Misfiring of individual combustion tubes will significantly lower performance or even stop the engine. Hence, an operation of such an engine requires a misfiring detection. Here, a model-based approach is used which exploits the innovation sequence calculated by a Kalman filter. The model necessary for the Kalman filter is determined based on a modal identification technique. A surrogate, nonreacting experimental set-up is considered in order to develop and test these methods.

scholarly journals An Initial Value Estimation Method for the Kalman and Extended Kalman Filters in Underground Metal Detection

2019 ◽  
Vol 9 (19) ◽  
pp. 4113 ◽  
Author(s):  
Yadong Wan ◽  
Zhen Wang ◽  
Peng Wang ◽  
Zhiyang Liu ◽  
Na Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Detection Methods ◽  
Polarizability Tensor ◽  
Filter Method ◽  
Excellent Performance ◽  
Initial Value ◽  
Initial State ◽  
Metal Detection ◽  
Value Estimation

As an underground metal detection technology, the electromagnetic induction (EMI) method is widely used in many cases. Therefore, the EMI detection algorithms with excellent performance are worth studying. One of the EMI detection methods in the underground metal detection is the filter method, which first obtains the secondary magnetic field data and then uses the Kalman filter (KF) and the extended Kalman filter (EKF) to estimate the parameters of metal targets. However, the traditional KF methods used in the underground metal detection have an unsatisfactory performance of the convergence as the algorithms are given a random or a fixed initial value. Here, an initial state estimation algorithm for the underground metal detection is proposed. The initial state of the target’s horizontal position is estimated by the first order central moments of the secondary field strength map. In addition, the initial state of the target’s depth is estimated by the full width at half maximum (FWHM) method. In addition, the initial state of the magnetic polarizability tensor is estimated by the least squares method. Then, these initial states are used as the initial values for KF and EKF. Finally, the position, posture and polarizability of the target are recursively calculated. A simulation platform for the underground metal detection is built in this paper. The simulation results show that the initial value estimation method proposed for the filtering algorithm has an excellent performance in the underground metal detection.

A System Identification Method for Excitation-Frequency Dependent Rotordynamic Coefficients

2012 ◽  
Author(s):  
Timothy W. Dimond ◽  
Amir A. Younan ◽  
Paul Allaire
Keyword(s):  
System Identification ◽  
Measurement Uncertainty ◽  
Excitation Frequency ◽  
Frequency Dependent ◽  
Rotordynamic Coefficients ◽  
Adjoint Systems ◽  
Dynamic Coefficients ◽  
Backward Whirl ◽  
Identification Technique ◽  
Damped Systems

Experimental identification of rotordynamic systems presents unique challenges. Gyroscopics, generally damped systems, and non-self-adjoint systems due to fluid structure interaction forces mean that symmetry cannot be used to reduce the number of parameters to be identified. Rotordynamic system experimental measurements are often noisy, which complicates comparisons with theory. When linearized, the resulting dynamic coefficients are also often a function of excitation frequency, as distinct from operating speed. In this paper, a frequency domain system identification technique is presented that addresses these issues for rigid-rotor test rigs. The method employs power spectral density functions and forward and backward whirl orbits to obtain the excitation frequency dependent effective stiffness and damping. The method is highly suited for use with experiments that employ active magnetic exciters that can perturb the rotor in the forward and backward whirl directions. Simulation examples are provided for excitation-frequency reduced tilting pad bearing dynamic coefficients. In the simulations, 20 and 50 percent Gaussian output noise was considered. Based on ensemble averages of the coefficient estimates, the 95 percent confidence intervals due to noise effects were within 1.2% of the identified value. The method is suitable for identification of linear dynamic coefficients for rotordynamic system components referenced to shaft motion. The method can be used to reduce the effect of noise on measurement uncertainty. The statistical framework can also be used to make decisions about experimental run times and acceptable levels of measurement uncertainty. The data obtained from such an experimental design can be used to verify component models and give rotordynamicists greater confidence in the design of turbomachinery.

Development of Control System by Nonlinear Compensation Using Digital Acceleration Control

2010 ◽  
Author(s):  
Takanori Emaru ◽  
Kazuo Imagawa ◽  
Yohei Hoshino ◽  
Yukinori Kobayashi
Keyword(s):  
Control System ◽  
System Identification ◽  
Mechanical System ◽  
Pid Control ◽  
Estimation Method ◽  
Proportional Integral Derivative ◽  
Inertia Term ◽  
Modeling Errors ◽  
Nonlinear Compensation ◽  
Simulation Results

Proportional-Integral-Derivative (PID) control has been most commonly used to operate mechanical systems. In PID control, however, there are limits to the accuracy of the resulting movement because of the influence of gravity, friction, and interaction of joints. We have proposed a digital acceleration control (DAC) that is robust over these modeling errors. One of the most practicable advantages of DAC is robustness against modeling errors. However, it does not always work effectively. If there are modeling errors in the inertia term of the model, the DAC controller cannot control a mechanical system properly. Generally an inertia term is easily modeled in advance, but it has a possibility to change. Therefore, we propose an online estimation method of an inertia term by using a system identification method. By using the proposed method, the robustness of DAC is considerably improved. This paper shows the simulation results of the proposed method using 2-link manipulator.

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