Ali, Machrus (2015) Optimisasi steering control mobil listrik auto pilot menggunakan adaptive neuro-fuzzy inference system (ANFIS) dan imperalist competitive algorithm (ICA). Masters thesis, Institut Teknologi Sepuluh Nopember.
Preview |
Text
undergraduated thesis.pdf Download (6MB) | Preview |
Abstract
Steering Control adalah sistem kemudi yang dirancang untuk akurasi
pergerakan steer terhadap lintasan kendaraan dan memperingan sistem kemudi.
Pada penelitian ini sistem kemudi menggunakan Adaptive Neuro-Fuzzy Inference
System (ANFIS). ANFIS dituning dari data training Proportional Integral
Derivative (PID) dengan Imperalist Competitive Algorithm (ICA). Adaptive
Neuro-Fuzzy Inference System (ANFIS) digunakan untuk mengendalikan Lateral
Motion pada model kendaraan. Pada penelitian ini akan dikembangkan model
Fully Automatic Steer By Wire System menggunakan 10 Degree Of Freedom
(DOF) terdiri dari 7-DOF Vehicle Ride Model dan 3-DOF Vehicle Handling
Model. Dari hasil ICA dibandingkan dengan Firefly Algorithm (FA), Particle
Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA)
menujukkan hasil yang paling baik. Dari hasil simulasi didapatkan bahwa PIDICA
jika dibandingkan dengan Firefly Algorithm (FA), Particle Swarm
Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA)
menujukkan hasil yang paling baik. Pada kecepatan default (13.8 km/h)
didapatkan nilai kp = 584,0150, ki = 4,1046, kd = 0,2014 dan mampu mengontrol
dengan kecpatan mencapai 69,0 km/h dengan overshot terkecil, yaitu 0,00621
pada C-RMS Error. Jika dibandingkan dengan hasil ANFIS hasil trainning data
PID-ICA, hasil ANFIS hampir sama atau sedikit lebih halus dengan overshot
0,00507 pada C-RMS Error meskipun ANFIS bertahan sampai kecepatan dibawah
69,0 km/h.
==================================================================================================
Steering Control is a steering system designed to steer the movement
accuracy of the trajectory of the vehicle and lighten the steering system. In this
study, the steering system using Adaptive Neuro-Fuzzy Inference System
(ANFIS). ANFIS tuned Proportional Integral Derivative (PID) of training data
with Imperialist Competitive Algorithm (ICA). Adaptive Neuro-Fuzzy Inference
System (ANFIS) is used to control the Lateral Motion on the vehicle model. This
research will be developed models Fully Automatic Steer By Wire System is
represented in a simulation of the active steering control using vehicle models
with 10 Degree Of Freedom (DOF) is composed of 7-DOF Model Vehicle Ride
and Handling 3-DOF Vehicle Model. ICA results compared with Firefly
Algorithm (FA), Particle Swarm Optimization (PSO), Ant Colony Optimization
(ACO), Bat Algorithm (BA) showed the best result. From the simulation results
showed that the PID-ICA When compared with Firefly Algorithm (FA), Particle
Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA)
showed the best result. In the default speed (13.8 km/h) obtained value Kp =
584,0150, Ki = 4,1046, Kd = 0,2014 and able to control the speed reaches 69.0
Km/h with the smallest overshot, that is 0.00621 on C-RMS Error. When
compared with the results of ANFIS training results of data PID-ICA, the results
of ANFIS softer with 0.00507 overshot the C-RMS Error although ANFIS lasted
until the speed below 69,0 Km/h.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTE 629.831 2 Ali o |
| Uncontrolled Keywords: | Vehicle, Lateral Motion, PID, ANFIS, ICA ============================================================================================== Vehicle, Lateral Motion, PID, ANFIS, ICA |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1001 Production of electric energy or power T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2785 Electric motors, Induction. |
| Divisions: | Faculty of Electrical Technology > Electrical Engineering > 20101-(S2) Master Thesis |
| Depositing User: | - Taufiq Rahmanu |
| Date Deposited: | 01 Apr 2019 02:57 |
| Last Modified: | 01 Apr 2019 02:57 |
| URI: | http://repository.its.ac.id/id/eprint/62658 |
Actions (login required)
 |
View Item |