Identification Inverted Pendulum System using Multilayer and Polynomial Neural Networks

• Published in: Revista IEEE América Latina Vol. 13; no. 5; pp. 1569 - 1576
• Main Authors: Lizarraga Orozco, Luis Mario, Ronquillo Lomeli, Guillermo, Rios Moreno, Jose Gabriel, Trejo Perea, Mario
• Format: Journal Article
• Language: English
• Published: Los Alamitos IEEE 01-05-2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Basis function; Biological neural networks; Computational modeling; Identification; Inverted pendulum; Mathematical model; Neural Networks; Nonhomogeneous media; Nonlinear system; Polynomials; RNA; Torque; Volterra polynomials
• ISSN: 1548-0992; 1548-0992
• DOI: 10.1109/TLA.2015.7112017

It is well known that the inverted pendulum can describe a variety of inherently unstable systems, which is a major reason to consider it as a benchmark problem in control and identification. In this paper, a comparison between two different kinds of neural networks is presented, on one hand the feed-forward multilayer network with back-propagation learning method, and in the other hand the Volterra polynomial basis function network. A Fuzzy Logic controller was implemented to stabilize the system around its operation point. Both neural networks were trained using the error between the model's output and the plant's actual output. The polynomial network shows better performance against the multilayer network