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Satrio Dewanto
Bina Nusantara University
Computer Science & IT Engineering Mathematics

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Journal : ComTech: Computer, Mathematics and Engineering Applications

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Step Respons Motor DC by Using Compression Signal Method Satrio Dewanto
ComTech: Computer, Mathematics and Engineering Applications Vol. 6 No. 3 (2015): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v6i3.2249

Abstract

Output signal in the form of a step response of a DC motor can be obtained by providing the input signal to the motor in the form of a signal step directly. Step response obtained in this way usually contains a lot of noise. To reduce noise, this research was designed with the aim to get the step response using signal compression method in which the input signal is not a step but a signal with a specific shape given to the motor and if the output signal of the motor is compressed, it will obtain impulse response. Step response of the DCmotor can be obtained by doing the integral to the impulse response. Step response which is obtained using signal compression method will be used to estimate parameters model of DC motor to see the validity of this method. The result of this research shows that the estimated value of the parameters from the step response has value closer to the value of the parameters given to the model of a DC motor. The conclusion of this research is the method of signal compression can be used to obtain the step response of a DC motor model. Further research will be conducted on the actual motor instead of using a mathematical model.
Pemodelan Dinamika Kendaraan dengan Jaringan Syaraf Tiruan Satrio Dewanto
ComTech: Computer, Mathematics and Engineering Applications Vol. 5 No. 1 (2014): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v5i1.2588

Abstract

Creating autonomous vehicle that can drive without a driver is a dream of the researchers who see the application of such a system will be needed in the future. Realizing such a system requires a dynamic model of the vehicle. It may be obtained by analytical method using dynamic equations. However, this way is rather difficult to do, especially in modeling the non-linear factors caused by tires, suspension, road conditions, etc. This study, in order to avoid difficulties in the analytical method, used artificial neural networks to model the dynamic system of the autonomous vehicle. It utilized data input from the camera sensor and vehicle speed.
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