NON-PARAMETRIC IDENTIFICATION TECHNIQUES FOR INTELLIGENT PNEUMATIC ACTUATOR

2015 ◽  
Vol 77 (20) ◽  
Author(s):  
Abdulrahman A. A. Emhemed ◽  
Rosbi Mamat ◽  
Ahmad ‘Athif Mohd Faudzi
Keyword(s):  
System Identification ◽  
Parametric Identification ◽  
Pneumatic Actuator ◽  
Step Response ◽  
Model Parameters ◽  
Force Model ◽  
Force System ◽  
Identification Technique ◽  
Identification Techniques ◽  
Non Parametric

The aim of this paper is to present experimental, empirical and analytic identification techniques, known as non-parametric techniques. Poor dynamics and high nonlinearities are parts of the difficulties in the control of pneumatic actuator functions, which make the identification technique very challenging. Firstly, the step response experimental data is collected to obtain real-time force model of the intelligent pneumatic actuator (IPA). The IPA plant and Personal Computer (PC) communicate through Data Acquisition (DAQ) card over MATLAB software. The second method is approximating the process by curve reaction of a first-order plus delay process, and the third method uses the equivalent n order process with PTn model parameters. The obtained results have been compared with the previous study, achieved based on force system identification of IPA obtained by the (Auto-Regressive model with eXogenous) ARX model. The models developed using non-parameters identification techniques have good responses and their responses are close to the model identified using the ARX system identification model. The controller approved the success of the identification technique with good performance. This means the Non-Parametric techniques are strongly recommended, suitable, and feasible to use to analyze and design the force controller of IPA system. The techniques are thus very suitable to identify the real IPA plant and achieve widespread industrial acceptance.

scholarly journals Evaluation of non-parametric identification techniques in second order models plus dead time

2020 ◽  
Vol 10 (6) ◽  
pp. 6340
Author(s):  
Carlos Robles-Algarín ◽  
Omar Rodríguez ◽  
Adalberto Ospino
Keyword(s):  
Cross Correlation ◽  
Dead Time ◽  
Parametric Identification ◽  
Absolute Error ◽  
Temperature Data ◽  
Second Order ◽  
The Other ◽  
Step Response ◽  
Identification Techniques ◽  
Non Parametric

In this paper, a set of non-parametric identification techniques are used in order to obtain second order models plus dead time for an underdamped system. Initially, non-parametric techniques were used to identify the system from the temperature data of a coal-heated oven. In this case, the identification techniques proposed by Stark, Jahanmiri - Fallahi and Ogata were used, which require obtaining two or three points of the step response for the system under study. In addition, the Matlab PID Tuner app was used to identify the underdamped system and compare the results with the other methods. The results show that the PID Tuner and the method proposed by Ogata are the ones that best represent the dynamics of the underdamped system, taking into account the values for the Integral Absolute Error (IAE) and the correlation coefficient. With the Stark method an IAE of 181.56 was obtained, while with the PID Tuner the best performance was achieved with an IAE of 21.59. In terms of the results obtained with the cross correlation, the best performance was achieved with the PID tuner and the Stark method.

Identification of ’Effective’ Linear Joints Using Coupling and Joint Identification Techniques

1998 ◽  
Vol 120 (2) ◽  
pp. 331-338 ◽  
Author(s):  
Y. Ren ◽  
C. F. Beards
Keyword(s):  
Dynamic Properties ◽  
Real Life ◽  
Model Parameters ◽  
Coupling Technique ◽  
Alternative Approach ◽  
Identification Technique ◽  
Joint Identification ◽  
Stiff Joint ◽  
Almost All ◽  
Identification Techniques

Almost all real-life structures are assembled from components connected by various types of joints. Unlike many other parts, the dynamic properties of a joint are difficult to model analytically. An alternative approach for establishing a theoretical model of a joint is to extract the model parameters from experimental data using joint identification techniques. The accuracy of the identification is significantly affected by the properties of the joints themselves. If a joint is stiff, its properties are often difficult to identify accurately. This is because the responses at both ends of the joint are linearly-dependent. To make things worse, the existence of a stiff joint can also affect the accuracy of identification of other effective joints (the term “effective joints” in this paper refers to those joints which otherwise can be identified accurately). This problem is tackled by coupling these stiff joints using a generalized coupling technique, and then the properties of the remaining joints are identified using a joint identification technique. The accuracy of the joint identification can usually be improved by using this approach. Both numerically simulated and experimental results are presented.

Step Response Identification for a Batch Reaction Vegetable Oil Transesterification

2014 ◽  
Vol 974 ◽  
pp. 357-361
Author(s):  
Tri Rachmanto ◽  
David Allanson ◽  
Christian Matthews
Keyword(s):  
System Identification ◽  
Sunflower Oil ◽  
Cross Correlation ◽  
Impedance Measurement ◽  
Parametric Identification ◽  
Step Response ◽  
Develop Model ◽  
Model Structure ◽  
Validation Test ◽  
Parameter Estimations

This paper discusses parameter estimations of step response from biodiesel transesterification process. The estimates signal generated from impedance measurement from sunflower oil transesterification process. 15 kHz AC signal has used to characterize the capacitance value during the process. The recorded signal then processed using Matlab system identification toolbox. The parametric identification method is selected to develop model structure. The model structure has modelled based on three different structures, ARMAX, BJ and ARX. The algorithm generated from identification toolbox as well as validation test. The validation test including correlation and cross correlation test. ARMAX structure appeared to be best between the others structure.

Validity Evaluations of On-Line Driver Modeling and State Assessment

2007 ◽  
Author(s):  
Liang-Kuang Chen ◽  
Meng-Hsuan Peng
Keyword(s):  
System Identification ◽  
Driving Simulator ◽  
State Assessment ◽  
Model Parameters ◽  
Steering Control ◽  
Line System ◽  
Validity Indices ◽  
On Line ◽  
Identification Techniques ◽  
Line Driver

Although driver steering control models and on-line model identification have been studied extensively, the application of the driver models to driver state assessment is seldom investigated. Furthermore, the validity level, or confidence index, of the on-line modeling and assessment of driver behavior is not reported in the literature. In this paper, on-line system identification techniques are applied to the determination of driver model parameters and model validity estimation. The driver steering control model is estimated on-line using system identification techniques. The on-line driver state assessment is achieved using probabilistic neural network (PNN), and the validity of the assessment is derived from the likelihood function inside the PNN. Preliminary results show that the computed validity indices agree with expectation reasonably well. More driving simulator experiments will be conducted to validate the proposed indices.

Fault Detection and Optimal Sensor Location in Vehicle Suspensions

2003 ◽  
Vol 9 (3-4) ◽  
pp. 337-359 ◽  
Author(s):  
P. Metallidis ◽  
G. Verros ◽  
S. Natsiavas ◽  
C. Papadimitriou
Keyword(s):  
System Identification ◽  
Fault Detection ◽  
Piecewise Linear ◽  
Parametric Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Vehicle Suspensions ◽  
Simulated Test ◽  
Sensor Configuration

A statistical system identification methodology is applied for performing parametric identification and fault detection studies in nonlinear vehicle systems. The vehicle nonlinearities arise due to the function of the suspension dampers, which assume a different damping coefficient in tension than in compression. The suspension springs may also possess piecewise linear characteristics. These lead to models with parameter discontinuities. Emphasis is put on investigating issues of unidentifiability arising in the system identification of nonlinear systems and the importance of sensor configuration and excitation characteristics in the reliable estimation of the model parameters. A methodology is proposed for designing the optimal sensor configuration (number and location of sensors) so that the corresponding measured data are most informative about the condition of the vehicle. The effects of excitation characteristics on the quality of the measured data are systematically explored. The effectiveness of the system identification and the optimal sensor configuration design methodologies is confirmed using simulated test data from a classical two-degree-of-freedom quartercar model as well as from more involved and complete vehicle models, including four-wheel vehicles with flexible body.

Experimental Nonlinear Model Identification of a Highly Nonlinear Resonator

2018 ◽  
Vol 140 (3) ◽  
Author(s):  
Tanju Yildirim ◽  
Jiawei Zhang ◽  
Shuaishuai Sun ◽  
Gursel Alici ◽  
Shiwu Zhang ◽  
...  
Keyword(s):  
Large Deformation ◽  
Model Identification ◽  
Model Parameters ◽  
Highly Nonlinear ◽  
Frequency Domains ◽  
Nonlinear Resonator ◽  
Identification Technique ◽  
Nonlinear Force ◽  
Identification Techniques ◽  
Time And Frequency Domains

In this work, two model identification methods are used to estimate the nonlinear large deformation behavior of a nonlinear resonator in the time and frequency domains. A doubly clamped beam with a slender geometry carrying a central intraspan mass when subject to a transverse excitation is used as the highly nonlinear resonator. A nonlinear Duffing equation has been used to represent the system for which the main source of nonlinearity arises from large midplane stretching. The first model identification technique uses the free vibration of the system and the Hilbert transform (HT) to identify a nonlinear force–displacement relationship in the large deformation region. The second method uses the frequency response of the system at various base accelerations to relate the maximum resonance frequency to the nonlinear parameter arising from the centerline extensibility. Experiments were conducted using the doubly clamped slender beam and an electrodynamic shaker to identify the model parameters of the system using both of the identification techniques. It was found that both methods produced near identical model parameters; an excellent agreement between theory and experiments was obtained using either of the identification techniques. This follows that two different model identification techniques in the time and frequency domains can be employed to accurately predict the nonlinear response of a highly nonlinear resonator.

scholarly journals Comparing system identification techniques for identifying human-like walking controllers

2021 ◽  
Vol 8 (12) ◽  
Author(s):  
Dave Schmitthenner ◽  
Anne E. Martin
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Linear System ◽  
Nonlinear System Identification ◽  
Ordinary Least Squares ◽  
Human Gait ◽  
Slip Model ◽  
Identification Technique ◽  
Experimental Values ◽  
Identification Techniques

While human walking has been well studied, the exact controller is unknown. This paper used human experimental walking data and system identification techniques to infer a human-like controller for a spring-loaded inverted pendulum (SLIP) model. Because the best system identification technique is unknown, three methods were used and compared. First, a linear system was found using ordinary least squares. A second linear system was found that both encoded the linearized SLIP model and matched the first linear system as closely as possible. A third nonlinear system used sparse identification of nonlinear dynamics (SINDY). When directly mapping states from the start to the end of a step, all three methods were accurate, with errors below 10% of the mean experimental values in most cases. When using the controllers in simulation, the errors were significantly higher but remained below 10% for all but one state. Thus, all three system identification methods generated accurate system models. Somewhat surprisingly, the linearized system was the most accurate, followed closely by SINDY. This suggests that nonlinear system identification techniques are not needed when finding a discrete human gait controller, at least for unperturbed walking. It may also suggest that human control of normal, unperturbed walking is approximately linear.

Prediction of the Hydrodynamic Parameters in the Coupled Heave and Pitch Motion Equations for Underwater Robotic Vehicles Using Measured Responses at Sea

2001 ◽  
Vol 123 (3) ◽  
pp. 93-102 ◽  
Author(s):  
Ayman B. Mahfouz ◽  
Mahmoud R. Haddara ◽  
Christopher D. Williams
Keyword(s):  
Neural Networks ◽  
Parametric Identification ◽  
Computer Time ◽  
Random Waves ◽  
Pitch Motion ◽  
Hydrodynamic Parameters ◽  
Identification Technique ◽  
Coupling Parameters ◽  
Robotic Vehicle ◽  
Identification Techniques

A method for the identification of the damping, restoring, and coupling parameters in the equations describing the coupled heave and pitch motions for an underwater robotic vehicle (URV) sailing near sea surface in random waves using only its measured responses at sea is presented. The random decrement equations are derived for the URV performing coupled heave and pitch motions in random waves. The hydrodynamic parameters in these equations are identified using a new identification technique called RDLRNNT, which uses a combination of a multiple linear regression algorithm and a neural networks technique. The combination of the classical parametric identification techniques and the neural networks technique provides robust results and does not require a large amount of computer time. The developed identification technique would be particularly useful in identifying the parameters for both moderately and lightly damped motions under the action of unknown excitations effected by a realistic sea. Numerically generated data for the coupled heave and pitch motion of a URV are used initially to test the accuracy of the technique. Experimental data are also used to validate the identification technique. It is shown that the developed technique is reliable in the identification of the parameters in the equations describing the coupled heave and pitch motions for an URV.

Sign in / Sign up

Export Citation Format

Share Document