Hardware simulation of automatic braking system based on fuzzy logic control

Basjaruddin, Noor Cholis; Kuspriyanto, Kuspriyanto; Suhendar, Suhendar; Saefudin, Didin; Azis, Virna Apriani

doi:10.14203/j.mev.2016.v7.1-6

Cited by 18 publications

(8 citation statements)

References 7 publications

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“…The matrix Q can be determined arbitrary, whereas the matrix R is determined based on variance of the raw data measurement distance can be calculated using Microsoft Excel. Furthermore, using the parameters that have been determined, it can be substituted into (4)…”

Section: (12) (13)mentioning

confidence: 99%

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Kalman Filter Algorithm Design for HC-SR04 Ultrasonic Sensor Data Acquisition System

Tahtawi

2018

IJITEE

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In the control system application, the existence of noise measurement may impact on the performance degradation. The noise measurement of the sensor is produced due to several reasons, such as the low specification, external signal disturbances, and the complexity of measured state. Therefore, it should be avoided to achieve the good control performance. One of the solutions is by designing a signal filter. In this paper, the design of Kalman Filter (KF) algorithm for ultrasonic range sensor is presented. KF algorithm is designed to overcome the existence of noise measurement on the sensor. The type of ultrasonic range sensor used is HC-SR04 which is capable to detect the distance from 2 cm to 400 cm. The discrete KF algorithm is implemented using ATMega 328p microcontroller on Arduino Uno board. The algorithm is then tested with different three covariance values of process noise. The test result shows that the KF algorithm is able to reduce the measurement noise of the ultrasonic sensor. The analysis of variance conducted shows that the smaller value of covariance matrix of the process and measured noises, the better filtering process performed. However, this results in a longer generated response time. Thus, an optimization is required to obtain the best filtering performance.

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Section: (12) (13)mentioning

confidence: 99%

“…This sensor is one type of optical sensor which serves to detect the distance. Ultrasonic sensors are widely used for control systems and computer applications such as control systems, and water level monitor [2], firefighting robot [3], and the automatic vehicle braking system [4].…”

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confidence: 99%

Kalman Filter Algorithm Design for HC-SR04 Ultrasonic Sensor Data Acquisition System

Tahtawi

2018

IJITEE

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“…ALC adalah salah satu sub sistem dari Advanced Driver Assistance Systems (ADAS) yaitu perangkat yang dapat membantu pengemudi sehingga perjalanan bisa selamat dan nyaman [8]. Sub sistem ADAS lainnya antara lain Automatic Braking System (ABS) [9], Adaptive Cruise Control (ACC) [10], Lane Keeping Assist System (LKAS) [11] dan [12], dan Overtaking Assistant System (OAS) [13].…”

Section: Pendahuluanunclassified

Adaptive Light Control Berbasis Kendali Fuzzy

Basjaruddin¹,

Saefudin

Fredian

et al. 2018

JRE

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Abstrak-Lampu mobil standar akan bersinar dengan intensitas tetap meskipun cahaya lingkungan berubah-ubah.Intensitas lampu yang tinggi kadang membuat pengemudi lain silau dan berpotensi menyebabkan kecelakaan. Adaptive Light Control (ALC) adalah sistem pengatur lampu depan mobil yang secara otomatis dapat menyesuaikan intensitas lampu sesuai dengan intensitas cahaya lingkungan. Pada penelitian ini dikembangkan ALC yang mampu mengatur intensitas lampu sesuai dengan intensitas cahaya lingkungan termasuk cahaya lampu mobil di depan. Pengaturan lampu dilakukan menggunakan sistem kendali fuzzy dengan memanfaatkan intensitas cahaya lingkungan dan jarak mobil terhadap objek sebagai masukan. Sistem ALC yang dikembangkan diuji coba pada mobil remote control yang dilengkapi sensor dan mikrokontroler. Hasil pengujian menunjukkan bahwa ALC yang dikembangkan dapat bekerja sesuai rancangan. Kata kunci: Adaptive Light Control, logika fuzzy, lampu depan mobilAbstract-Standard headlights will shine with a fixed intensity even though the environmental light changes. Highintensity lights sometimes make other drivers glare and potentially cause accidents. Adaptive Light Control (ALC) is a car headlights control system that can automatically adjust the intensity of the lamp according to the intensity of the light environment. In this research, we developed ALC which was able to regulate the intensity of the lamp according to the intensity of the environmental light including the light of the car in front. Light settings are performed using a fuzzy control system by utilising environmental light intensity and the distance of the car to the object as input. The developed ALC system was tested on remote control cars equipped with sensors and microcontrollers. The intensity of headlight begins to fade on the distance of the car to the object of 92 cm for objects in front of, 108 cm for objects on the left, and 22 cm for objects on the right.

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“…Some studies developed RCAS algorithms, among others [9], [10], while others researched the RCAS sensor [11], [12] and driver behaviour when a rear-end collision occurs, among others [13]. The development of RCAS as a subsystem of ADAS and the driverless car is also related to the development of other ADAS subsystems such as the Automatic Braking System (ABS) [14], Adaptive Cruise Control (ACC) [15], Lane Keeping Assist System LKAS [16], [17], and Overtaking Assistant System (OAS) [18].…”

Section: Introductionmentioning

confidence: 99%

Hardware Simulation of Rear-End Collision Avoidance System Based on Fuzzy Logic

Basjaruddin¹,

Saefudin²,

Pancawati³

2020

JRE

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Rear-end collisions are the most common type of traffc accident. On the highway, a real-end collision may involve more than two vehicles and cause a pile-up or chain-reaction crash. Referring to data released by the Australian Capital Territory (ACT), rear-end collisions which occurred throughout 2010 constituted as much as 43.65% of all collisions. In most cases, these rear-end collisions are caused by inattentive drivers, adverse road conditions and poor following distance. The Rear-end Collision Avoidance System (RCAS) is a device to help drivers to avoid rear-end collisions. The RCAS is a subsystem of Advanced Driver Assistance Systems (ADASs) and became an important part of the driverless car. This paper discusses a hardware simulation of a RCAS based on fuzzy logic using a remote control car. The Mamdani method was used as a fuzzy inference system and realized by using the Arduiono Uno microcontroller system. Simulation results showed that the fuzzy logic algorithm of RCAS can work as designed.

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Hardware simulation of automatic braking system based on fuzzy logic control

Cited by 18 publications

References 7 publications

Kalman Filter Algorithm Design for HC-SR04 Ultrasonic Sensor Data Acquisition System

Kalman Filter Algorithm Design for HC-SR04 Ultrasonic Sensor Data Acquisition System

Adaptive Light Control Berbasis Kendali Fuzzy

Hardware Simulation of Rear-End Collision Avoidance System Based on Fuzzy Logic

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