Neural network based adaptive compensator for motion/force control of constrained mobile manipulators with uncertainties

Author

Singh, H.P. ; Sukavanam, N.

Author_Institution

Dept. of Math., Indian Inst. of Technol., Roorkee, India

fYear

2011

fDate

5-8 Dec. 2011

Firstpage

253

Lastpage

258

Abstract

The aim of this paper is to design a neural network (NN) based motion/force control scheme for holonomic constrained nonholonomic mobile manipulators with model uncertainties and external disturbances. For compensator design, the prior knowledge of the bound of uncertainty is not required but we estimate this bound by using feedforward neural network. The constraint force converge to the desired force and the Lyapunov stability analysis will be used to show that the tracking error as well as neural network weight error are uniformly ultimately bounded. Simulation studies are carried out for a two wheels mobile manipulator to show the effectiveness of the proposed control scheme.

Keywords

Lyapunov methods; adaptive control; compensation; feedforward neural nets; force control; manipulators; mobile robots; motion control; neurocontrollers; stability; uncertain systems; Lyapunov stability analysis; constraint force converge; external disturbances; feedforward neural network; force control; holonomic constrained nonholonomic mobile manipulators; model uncertainties; motion control; neural network based adaptive compensator; neural network weight error; tracking error; Force; Manipulator dynamics; Mobile communication; Trajectory; Uncertainty; Vectors; Holonomic constraint; Lyapunov stability analysis; Neural network based adaptive compensator; Nonholonomic mobile manipulators;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems (HIS), 2011 11th International Conference on

Conference_Location

Melacca

Print_ISBN

978-1-4577-2151-9

Type

conf

DOI

10.1109/HIS.2011.6122114

Filename

6122114