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.

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.

Analog methods for on-line system identification using noisy measurements

SIMULATION ◽  
1968 ◽  
Vol 11 (5) ◽  
pp. 241-248 ◽  
Author(s):  
D.W. Ricker ◽  
G.N. Saridis
Keyword(s):  
System Identification ◽  
Nonlinear Systems ◽  
Automatic Control ◽  
Stochastic Approximation ◽  
Linear Time ◽  
Measurement Noise ◽  
Current Interest ◽  
Line System ◽  
On Line ◽  
Noisy Measurements

Of current interest in the field of automatic control is the problem of system identification in the presence of measurement noise. Generally this problem has been dis cussed in the literature for the case of linear time-invar iant systems where the parameters to be identified are constant or slowly varying. This paper describes the ap plication of continuous stochastic approximation meth ods for the identification of a class of simple nonlinear systems. The two algorithms described are easily imple mented with analog equipment, although one of them requires some logic capability.

On-line system identification with an adjustable estimation interval

1988 ◽  
Vol 19 (10) ◽  
pp. 1955-1967 ◽  
Author(s):  
WEN-TENG WU ◽  
WEI-HSIUNG OU ◽  
KUO-CHIEH CHEN
Keyword(s):  
System Identification ◽  
Line System ◽  
On Line ◽  
Estimation Interval
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