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

David Ricardo Cruz | Zendy; Salatiel García | Zendy; Manuel Bandala | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

David Ricardo Cruz,

Salatiel García,

Manuel Bandala

Publication year - 2016

Publication title -

international journal of advanced robotic systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.394

H-Index - 46

eISSN - 1729-8814

pISSN - 1729-8806

DOI - 10.5772/63485

Subject(s) - inverted pendulum , computer science , software deployment , control theory (sociology) , robot , artificial neural network , control (management) , algorithm , artificial intelligence , physics , quantum mechanics , nonlinear system , operating system

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

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research