An Improved Dynamic Neurocontroller Based on Christoffel Symbols

Author

Mulero-Martinez, J.I.

Author_Institution

Departamento de Ingenieria de Sistemas y Automatica, Univ. Politecnica de Cartagena

Volume

18

Issue

3

fYear

2007

fDate

5/1/2007 12:00:00 AM

Firstpage

865

Lastpage

879

Abstract

In this paper, a dynamic neurocontroller for positioning of robots based on static and parametric neural networks (NNs) has been developed. This controller is based on Christoffel symbols of first kind in order to carry out coriolis/centripetal matrix. Structural properties of robots and Kronecker product has been taken into account to develop NNs to approximate nonlinearities. The weight updating laws have been obtained from a nonlinear strategy based on Lyapunov energy that guarantees both stability and boundedness of signals and weights. The NN weights are tuned online with no "offline learning phase" and are initialized to zero. The neurocontroller improves the implementation with respect to other dynamic NNs used in the literature

Keywords

Lyapunov methods; control nonlinearities; manipulators; matrix algebra; neurocontrollers; position control; Christoffel symbols; Kronecker product; Lyapunov energy; centripetal matrix; coriolis matrix; dynamic neurocontroller; parametric neural networks; robot positioning; weight updating laws; Adaptive control; Control systems; Fourier series; Neural networks; Neurocontrollers; Nonlinear control systems; Robots; Stability; Three-term control; Uncertainty; Christoffel symbols; Kronecker product; Lyapunov energy; neural network (NN) controller; position control of robots; Algorithms; Artificial Intelligence; Computer Simulation; Decision Support Techniques; Information Storage and Retrieval; Kinetics; Models, Theoretical; Neural Networks (Computer); Pattern Recognition, Automated; Robotics;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2007.894070

Filename

4182399