Nonlinear Model Predictive Control (NMPC) Pada Kendali Mobil Tanpa Awak Untuk Menghindari Halangan Bergerak

Nasichah, Nasichah (2021) Nonlinear Model Predictive Control (NMPC) Pada Kendali Mobil Tanpa Awak Untuk Menghindari Halangan Bergerak. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

[img] Text
06111740000003-Undergraduate_Thesis.pdf - Accepted Version
Restricted to Repository staff only until 1 October 2023.

Download (2MB) | Request a copy

Abstract

Indonesia merupakan sebuah negara dengan jumlah penduduk terbanyak keempat di dunia yang mempunyai resiko yang lebih besar dalam meningkatnya angka kematian akibat kecelakaan lalu lintas. Penggunaan kendaraan tanpa awak dapat dijadikan solusi dalam mengurangi kecelakaan lalu lintas. Supaya kendaraan tanpa awak dapat beroperasi tanpa campur tangan manusia, kendaraan tanpa awak harus mampu mendeteksi kondisi lingkungan, memprediksi pergerakan objek di sekitarnya, dan membuat jalur ke tujuannya sambil menghindari rintangan statis maupun dinamis yang ada di sekitarnya. Pada penelitian ini, dikaji mengenai model nonlinear mobil tanpa awak. Model yang digunakan yaitu model nonlinear kinematic bicycle. Selanjutnya, dilakukan pendiskritan pada model menggunakan Metode Euler yang kemudian diterapkan metode NMPC untuk memperoleh kendali optimal pada sudut kemudi mobil dan percepatan throttle. Kendali tersebut diterapkan dengan tujuan mobil dapat melakukan penghindaran terhadap halangan bergerak yang berada di depannya sehingga tidak terjadi tabrakan, terutama tabrakan bagian belakang. Hasil dalam penelitian ini adalah NMPC mampu digunakan dalam mengendalikan posisi dan kecepatan mobil menuju referensi yang diinginkan. Dari beberapa skenario yang dipertimbangkan, kendali bekerja paling optimal dilihat dari nilai MAE terkecil adalah ketika kecepatan halangan kurang dari kecepatan awal mobil tanpa awak. ================================================================================================================== Indonesia is a country with the fourth largest population in the world which has a greater risk of increasing mortality due to traffic accidents. The use of autonomous vehicles can be used as a solution in reducing traffic accidents. In order for an autonomous vehicle to operate without human intervention, an autonomous vehicle must be able to detect environmental conditions, predict the movement of objects in the vicinity, and create a path to its destination while avoiding static and dynamic obstacles in the vicinity. In this study, the nonlinear model of an autonomous car is studied. The model used is a nonlinear kinematic bicycle model. Furthermore, the discrete model is performed using the Euler Method which is then applied to the Nonlinear Model Predictive Control (NMPC) method to obtain optimal control of the car's steering angle and acceleration of throttle. This control is implemented with the aim of the car being able to avoid moving obstacles in front of it so that a collision does not occur, especially a rear collision. Thus, the result in this study is that NMPC can be used in controlling the position and speed of the car towards the desired references. Of the several considered scenarios, the control work most optimally as seen from the lowest MAE value is when the obstacle’s speed is less than the initial speed of autonomous car.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: desain kendali, Euler, mobil tanpa awak, NMPC, control design, Euler, autonomous car
Subjects: Q Science
Q Science > Q Science (General) > Q180.55.M38 Mathematical models
Q Science > QA Mathematics
T Technology > TJ Mechanical engineering and machinery > TJ217.6 Predictive Control
T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL152.8 Vehicles, Remotely piloted. Autonomous vehicles.
Divisions: Faculty of Mathematics, Computation, and Data Science > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User: Nasichah Nasichah
Date Deposited: 03 Sep 2021 02:52
Last Modified: 03 Sep 2021 02:52
URI: https://repository.its.ac.id/id/eprint/90033

Actions (login required)

View Item View Item