Robot control with a fully tuned Growing Radial Basis Function neural network

Autor:	Yi Luo, Abraham K. Ishihara, Yoo Hsiu Yeh
Rok vydání:	2011
Předmět:	Computer Science::Robotics Lyapunov function symbols.namesake Radial basis function network Discretization Computer science Radial basis function neural Control theory Sliding window protocol Robot manipulator symbols Radial basis function Robot control
Zdroj:	IJCNN
DOI:	10.1109/ijcnn.2011.6033241
Popis:	A fully tuned Growing Radial Basis Function (GRBF) neural network controller for the control of robot manipulators is proposed. In addition to the weights, the centers and the standard variations are adapted online. Furthermore, we present an algorithm in which nodes of the network are appended based on sliding window performance criteria. Lyapunov analysis is used to show uniform ultimate boundedness and a discretization method is used to derive the growing algorithm. Simulations of a 2-DOF planar robot arm are presented to illustrate the method.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::cab2a9b89a47ab6c4794ecf8f3780622 https://doi.org/10.1109/ijcnn.2011.6033241 Zobrazit plný text záznamu