Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-21T18:58:52.129Z Has data issue: false hasContentIssue false

A proof of the Ge–Lee statement on the inertia regressor of robot manipulators

Published online by Cambridge University Press:  16 March 2012

Juan Ignacio Mulero-Martínez*
Affiliation:
Departamento de Ingeniería de Sistemas y Automática. Universidad Politécnica de Cartagena. Campus Muralla del Mar. Cartagena, 30203, Spain
*
*Corresponding author. E-mail: [email protected]

Summary

This paper delivers a proof of a statement due to Ge and Lee. Specifically, these authors stated, without proof, that the entries of the inertia matrix may be completely parameterized by stacking elements of a regressor superset. This superset has the advantage of avoiding to derive the complete dynamics of a robot manipulator. On the basis of both mechanics and combinatorial arguments, we deliver a formal proof. In addition, we improve the estimations by sorting joint variables and partitioning the inertia matrix that results into the reduction of the regressor superset. The number of nonlinear functions in the regressor is also quantified. A 2 degrees of freedom revolute robot is presented to illustrate these ideas.

Type
Articles
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Lu, W. S. and Meng, Q. H., “Regressor formulation of robot dynamics: Computation and applications,” IEEE Trans. Robot. Autom. 9 (3), 323333 (June 1993).CrossRefGoogle Scholar
2.Lu, W. S. and Meng, Q. H., “Recursive Computation of Manipulator Regressor and Its Application to Adaptive Motion Control of Robots,” Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, Victoria, BC, Canada (May 1991).Google Scholar
3.Lewis, F. L., Yesildirek, A. and Liu, K., “Neural net robot controller: Structure and stability proofs,” J. Intell. Robot. Syst. 13, 123 (1995).Google Scholar
4.Lewis, F. L., Liu, K. and Yesildirek, A., “Neural net robot controller with guaranteed tracking performance,” IEEE Trans. Neural Netw. 6 (3), 703716 (May 1995).CrossRefGoogle ScholarPubMed
5.Lewis, F. L., Yesildirek, A. and Liu, K., “Multilayer neural net robot controller with guaranteed tracking performance,” IEEE Trans. Neural Netw. 7 (2), 112 (March 1996).CrossRefGoogle ScholarPubMed
6.Ge, S. S., Hang, C. C. and Woon, L. C., “Experimental studies of network-based adaptive control of robot manipulators,” J. Inst. Eng. Singap. 37 (1), 4048 (1997).Google Scholar
7.Ge, S. S. and Hang, C. C., “Network Modelling of Rigid Body Robots,” In: Proceedings of the 2nd Asian Control Conference (AsCC), Seoul, South Korea, (Jul. 22–25, 1997) vol. 3, pp. 251254.Google Scholar
8.Ge, S. S., Lee, T. H. and Harris, C. J., Adaptive Neural Network Control of Robotic Manipulators (World Scientific, London, 1998).CrossRefGoogle Scholar
9.Horn, R. A. and Johnson, C. R., Topics in Matrix Analysis (Cambridge University Press, Cambridge, UK, 1999).Google Scholar
10.Fu, K. S., Gonzalez, R. C. and Lee, C. S. G., Robotics: Control, Sensing, Vision and Intelligence (McGraw-Hill, Columbus, OH, 1986).Google Scholar