Identification approach for a production system using ARX model

Rachad, Sofia; Fouraiji, Hicham; Bensassi, Bahloul

doi:10.1109/gol.2014.6887422

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…We will be interested in the following on parametric models. previous work, we treated the case of only one parametric model ARX [20]. In this case, we will compare the performances of two parametric models ARX and ARMAX.…”

Section: Description Of Production Systemmentioning

confidence: 98%

Modeling a production system by parametric identification approach

Rachad

Fouraiji

Bensassi

2014

2014 Second World Conference on Complex Systems (WCCS)

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This paper refers to possibility of utilization a new method for modeling an industrial production system using the identification approach.The production system studied is considered as a black box, which does not require any knowledge of the internal parameters. Two structures of linear parametric models are outlined and compared i.e. Autoregressive with Exogenous (ARX) model and Autoregressive Moving Average with Exogenous (ARMAX) model. The best linear model is ARMAX, due to its higher percentage of best fit.

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Section: Description Of Production Systemmentioning

confidence: 98%

Modeling a production system by parametric identification approach

Rachad

Fouraiji

Bensassi

2014

2014 Second World Conference on Complex Systems (WCCS)

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“…[7] System identification is a technique for determining the mathematical description of a system by analyzing the system's input and output. Models with the Auto Regressive structure with exogenous input (ARX) are often used because of their simplicity and efficiency [8], [9]. The ARX model system's identification is accomplished out using discrete measurement data.…”

Section: Ii1 Bldc Motor System Identificationmentioning

confidence: 99%

Measurement, Modeling, and Optimization Speed Control of BLDC Motor Using Fuzzy-PSO Based Algorithm

Anshory

Hadidjaja

Sulistiyowati

2021

JET

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Measurement, modeling, and optimization are three important components that must be done to get a better system on the BLDC motor speed control system. The problem that arises in the BLDC motor speed control system is the instability indicated by a high overshoot value, a slow rise time value, and a high error steady-state. The purpose of this study is to increase the stability indicator by eliminating the high value of overshoot and error steady-state and increasing the value of the rise time. The method used in this research is to measure the input and output physical parameters, to model the BLDC motor plant mathematically and the last is to perform optimization using several control methods such as Proportional Integral Derivative (PID) control, fuzzy logic intelligent control, and Particle Swarm Optimization algorithm. (PSO). Experimental and simulation results show that the PSO algorithm has a better value in increasing stability indicators when compared to the other two control methods with a rise time of 0.00121 seconds, settling time of 0.00241 seconds, and overshoot of 0%.

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“…In order to study the effect of disturbance on the system output, then MISO (Multi-input single output) system can be presented as two transfer functions, first one describes the relation between first input which is the disturbance and the output voltage, and the other transfer function describes the relation between the second input which is the reference voltage, and the output voltage [5][6][7][8][9][10][11][12][13][14]. Fig.…”

Section: Dc-dc Chopper Circuit Modelsmentioning

confidence: 99%

Online Parameter Estimation of DC-DC Converter through OPC Communication Channel

Obeidat¹,

Anani²,

Mansour³

2021

IJACSA

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System identification is a very powerful tool for determining the system model and parameters from sets of observable input and output data. Once the system parameters are obtained, the system dynamic behavior, including all the system characteristics (time constant, overshoot, settling time, etc.) can be accessed and evaluated. Despite the difficulty and communication channel lag, online parameter estimation outperforms offline system identification due to the ability to remotely monitor and control the system as well as improve the system's controller, making it more accurate and reliable. With the extreme development in technology, the importance of combining wireless networks with closed automatic control systems has emerged. This connection facilitates communication processes between the different units in the control for remotely controlled of the output. However, there are some errors affecting such system resulted from communication channel, A/D and D/A conversion process, identification process, or the existence of adaptive weight Gaussian noise. In this paper, the errors were investigated using real system, and then a suitable controller was tuned and optimized in order to reduce and eliminate various errors. The results show excellent dynamic behavior of the system under transmitting and receiving process.

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Identification approach for a production system using ARX model

Cited by 5 publications

References 6 publications

Modeling a production system by parametric identification approach

Modeling a production system by parametric identification approach

Measurement, Modeling, and Optimization Speed Control of BLDC Motor Using Fuzzy-PSO Based Algorithm

Online Parameter Estimation of DC-DC Converter through OPC Communication Channel

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