scholarly journals Kontrol Attitude Unmanned Ground Vehicle (UGV) menggunakan Backpropagation Neural Network

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Ike Bayusari ◽  
Bhakti Yudho Suprapto
Keyword(s):  
Neural Network ◽  
Unmanned Ground Vehicle ◽  
Backpropagation Neural Network ◽  
Ground Vehicle

Unmaned Ground Vehicle (UGV) merupakan teknologi kendaraan darat tanpa awak yang berguna untuk mempermudah pekerjaan manusia dalam berbagai bidang seperti transportasi, aktivitas logistik industri, search and resque, pertahanan dan keamanan, juga beberapa bidang lainnya. Pengendalian attitude menjadi permasalahan karena membutuhkan ketelitian akibat adanya pengaruh kecepatan. Selain itu, bagaimana UGV tersebut mengikuti jalur yang ditentukan juga memerlukan pengendalian attitude yang optimal. Penelitian ini bertujuan untuk merancang dan menguji performa serta untuk mengatahui tingkat keberhasilan dan keakuratan pengendalian UGV menggunakan algoritma Backpropagaion Neural Network. Dari hasil pengujian didapatkan bahwa algoritma ini berhasil mengikuti data uji yang diberikan dengan nilai MSE yang kecil.

Evalaution and Control of Mirror Sliding Friction of Unmanned Ground Vehicle using Radial Basis Neural Network

2019 ◽  
Vol 11 (5) ◽  
Author(s):  
Xiaohui Yang ◽  
Jian Zhao
Keyword(s):  
Neural Network ◽  
Sliding Friction ◽  
Rbf Neural Network ◽  
Simulation Method ◽  
Vehicle Speed ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Disturbance Term ◽  
And Control ◽  
Msf Model

In order to effectively analyse the mirror sliding friction(MSF) degree of unmanned ground vehicle(UGV) and improve its anti-disturbance performance, a simulation method for MSF degree of UGV based on RBF neural network is proposed. A single-input and double-output RBF neural network is adopted to estimate the uncertain dynamic parameters of the MSF model. The obtained parameters are used to describe the MSF control law based on RBF neural network. An adaptive law based on slow time-varying disturbance characteristics is designed to estimate the total friction disturbance term in the MSF model online. The simulation results show that the proposed method can analyse the MSF degree of unmanned ground vehicle at different speeds and gradients. The influence of gradient on the decline rate of friction degree is greater than that of vehicle speed. The mean error of friction disturbance term calculated by the method is only about 0.9% which has the advantage of low error of friction degree estimation when compared to conventional methods.

Adaptive Neural Network Path Tracking of Unmanned Ground Vehicle

2006 ◽  
pp. 1233-1238 ◽  
Author(s):  
Xiaohong Liao ◽  
Zhao Sun ◽  
Liguo Weng ◽  
Bin Li ◽  
Yongduan Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Path Tracking ◽  
Unmanned Ground Vehicle ◽  
Adaptive Neural Network ◽  
Ground Vehicle

Design and Realization of an All-terrain Unmanned Ground Vehicle

ROBOT ◽  
2013 ◽  
Vol 35 (6) ◽  
pp. 657 ◽  
Author(s):  
Taoyi ZHANG ◽  
Tianmiao WANG ◽  
Yao WU ◽  
Qiteng ZHAO
Keyword(s):  
Unmanned Ground Vehicle ◽  
Ground Vehicle

Unmanned Ground Vehicle for Security and Surveillance

2020 ◽  
Author(s):  
Prajot P. Kulkarni ◽  
Shubham R. Kutre ◽  
Shravan S. Muchandi ◽  
Pournima Patil ◽  
Shankargoud Patil
Keyword(s):  
Unmanned Ground Vehicle ◽  
Ground Vehicle
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