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ANN-Based Control of a Wheeled Inverted Pendulum System Using an Extended DBD Learning Algorithm

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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ANN-Based Control of a Wheeled Inverted Pendulum System Using an Extended DBD Learning Algorithm. / Cruz, David; García, Salatiel; Bandala, Manuel.
In: International Journal of Advanced Robotic Systems, Vol. 13, No. 3, 24.05.2016.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Cruz, D, García, S & Bandala, M 2016, 'ANN-Based Control of a Wheeled Inverted Pendulum System Using an Extended DBD Learning Algorithm', International Journal of Advanced Robotic Systems, vol. 13, no. 3. https://doi.org/10.5772/63485

APA

Cruz, D., García, S., & Bandala, M. (2016). ANN-Based Control of a Wheeled Inverted Pendulum System Using an Extended DBD Learning Algorithm. International Journal of Advanced Robotic Systems, 13(3). https://doi.org/10.5772/63485

Vancouver

Cruz D, García S, Bandala M. ANN-Based Control of a Wheeled Inverted Pendulum System Using an Extended DBD Learning Algorithm. International Journal of Advanced Robotic Systems. 2016 May 24;13(3). Epub 2016 Jan 1. doi: 10.5772/63485

Author

Cruz, David ; García, Salatiel ; Bandala, Manuel. / ANN-Based Control of a Wheeled Inverted Pendulum System Using an Extended DBD Learning Algorithm. In: International Journal of Advanced Robotic Systems. 2016 ; Vol. 13, No. 3.

Bibtex

@article{6d68d65802f74da68e9003180345be0c,
title = "ANN-Based Control of a Wheeled Inverted Pendulum System Using an Extended DBD Learning Algorithm",
abstract = "This paper presents a dynamic model for a self-balancing vehicle using the Euler-Lagrange approach. The design and deployment of an artificial neuronal network (ANN) in a closed-loop control is described. The ANN is characterized by integration of the extended delta-bar-delta algorithm (DBD), which accelerates the adjustment of synaptic weights. The results of the control strategy in the dynamic model of the robot are also presented.",
keywords = "Artificial Neuronal Network, Euler-Lagrange, Extended Delta-bar-delta Algorithm, Self-balancing Robot",
author = "David Cruz and Salatiel Garc{\'i}a and Manuel Bandala",
year = "2016",
month = may,
day = "24",
doi = "10.5772/63485",
language = "English",
volume = "13",
journal = "International Journal of Advanced Robotic Systems",
issn = "1729-8806",
publisher = "SAGE Publications Inc.",
number = "3",

}

RIS

TY - JOUR

T1 - ANN-Based Control of a Wheeled Inverted Pendulum System Using an Extended DBD Learning Algorithm

AU - Cruz, David

AU - García, Salatiel

AU - Bandala, Manuel

PY - 2016/5/24

Y1 - 2016/5/24

N2 - This paper presents a dynamic model for a self-balancing vehicle using the Euler-Lagrange approach. The design and deployment of an artificial neuronal network (ANN) in a closed-loop control is described. The ANN is characterized by integration of the extended delta-bar-delta algorithm (DBD), which accelerates the adjustment of synaptic weights. The results of the control strategy in the dynamic model of the robot are also presented.

AB - This paper presents a dynamic model for a self-balancing vehicle using the Euler-Lagrange approach. The design and deployment of an artificial neuronal network (ANN) in a closed-loop control is described. The ANN is characterized by integration of the extended delta-bar-delta algorithm (DBD), which accelerates the adjustment of synaptic weights. The results of the control strategy in the dynamic model of the robot are also presented.

KW - Artificial Neuronal Network

KW - Euler-Lagrange

KW - Extended Delta-bar-delta Algorithm

KW - Self-balancing Robot

U2 - 10.5772/63485

DO - 10.5772/63485

M3 - Journal article

AN - SCOPUS:84994056150

VL - 13

JO - International Journal of Advanced Robotic Systems

JF - International Journal of Advanced Robotic Systems

SN - 1729-8806

IS - 3

ER -