A new factorization of the Coriolis/centripetal matrix

This paper provides a comprehensive description of a new method of factorization for the Coriolis/centripetal matrix. In the past three decades, studies on dynamics have rapidly developed through the efforts of many researchers in the field of mechanics. While direct methods for deriving the Coriolis/centripetal matrix are well known and have been widely used in the last century, the entries of this matrix were always obtained by means of the Christoffel symbols of first kind. Startling techniques for deriving dynamic equations of robot manipulators first appeared about 30 years ago. Since then, much has been done to refine and develop the method, but it is still a highly active field of research, with many outstanding problems, both theoretical and in applications. This work presents, in a unitary frame and from a new perspective, the main concepts and results of one of the most fascinating aspects of mechanics, namely the factorization of structures, and offers the reader another point of view concerning a possible way to approach the Coriolis/centripetal matrix. It aims to study a theory of representation for such a matrix based on an elegant method of fundamental matrices. The paper is intended to be self-contained by presenting complete properties emerging from these novel structures. This work is useful not only to researchers in mechanics, but also to control engineers who are interested in learning some of the mechanical modeling. Toward this end, the paper provides numerical examples, as well as practical adaptive applications for modern designers to use at the system level.

[1]  Frank L. Lewis,et al.  Control of Robot Manipulators , 1993 .

[2]  Mark W. Spong,et al.  Robot dynamics and control , 1989 .

[3]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.

[4]  Charles P. Neuman,et al.  The inertial characteristics of dynamic robot models , 1985 .

[5]  Charles P. Neuman,et al.  Properties and structure of dynamic robot models for control engineering applications , 1985 .

[6]  C. S. G. Lee,et al.  Robotics: Control, Sensing, Vision, and Intelligence , 1987 .

[7]  R. Paul Robot manipulators : mathematics, programming, and control : the computer control of robot manipulators , 1981 .

[8]  James S. Albus,et al.  Data Storage in the Cerebellar Model Articulation Controller (CMAC) , 1975 .

[9]  Juan Ignacio Mulero Martínez Uniform Bounds of the Coriolis/Centripetal Matrix of Serial Robot Manipulators , 2007, IEEE Trans. Robotics.

[10]  L. W. Tsai,et al.  Robot Analysis: The Mechanics of Serial and Parallel Ma-nipulators , 1999 .

[11]  Bruno Siciliano,et al.  Modelling and Control of Robot Manipulators , 1997, Advanced Textbooks in Control and Signal Processing.

[12]  Juan Ignacio Mulero Martínez An Improved Dynamic Neurocontroller Based on Christoffel Symbols , 2007, IEEE Trans. Neural Networks.

[13]  J. Wen,et al.  New class of control laws for robotic manipulators Part 1. Non–adaptive case , 1988 .

[14]  Juan Ignacio Mulero Martínez Bandwidth of mechanical systems and design of emulators with RBF , 2007, Neurocomputing.

[15]  Nader Sadegh,et al.  A perceptron network for functional identification and control of nonlinear systems , 1993, IEEE Trans. Neural Networks.

[16]  Juan Ignacio Mulero Martínez Canonical transformations used to derive robot control laws from a port-controlled Hamiltonian system perspective , 2008, Autom..

[17]  Robert J. Schilling,et al.  Fundamentals of Robotics , 1990 .