Development of Computational Intelligence-based Control System using Backpropagation Neural Network for Wheeled Robot

This study aims to develop an optimal autonomous control system for a three-wheeled robot with two motors. The focus of this research is a neural network direct inverse controller system that is trained using backpropagation algorithm. Autonomous control system training is carried out by using the r...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	2018 International Conference on Electrical Engineering and Computer Science (ICECOS) s. 101 - 106
Hlavní autoři:	Priandana, Karlisa, Abiyoga, Iqbal, Wulandari, Wahjuni, Sri, Hardhienata, Medria, Buono, Agus
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.10.2018
Témata:	Backpropagation Backpropagation neural network direct inverse controller Mobile robots Robot kinematics Robot sensing systems Testing Training wheeled robot
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	This study aims to develop an optimal autonomous control system for a three-wheeled robot with two motors. The focus of this research is a neural network direct inverse controller system that is trained using backpropagation algorithm. Autonomous control system training is carried out by using the real data of manually controlled wheeled robot. This study analyzed the use of two Backpropagation learning algorithms namely Levenberg Marquardt Backpropagation and Bayesian Regularization Backpropagation, and compares 3 different controller network configurations, i.e., 13-10-2, 13- 20-2 and 13-26-2. The simulation results revealed that the best control system network architecture is 13-10-2 which was trained using Bayesian Regularization Backpropagation algorithm. This result serves as early evidence that a neural network-based control system can be used for autonomous wheeled robots.
DOI:	10.1109/ICECOS.2018.8605183