Saturated Neural Adaptive Robust Output Feedback Control of Robot Manipulators:An Experimental Comparative Study

Authors

A. Chatraei Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

K. Shojaei Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

M. Pourrahim Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

O. Shahnazari Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Abstract:

In this study, an observer-based tracking controller is proposed and evaluatedexperimentally to solve the trajectory tracking problem of robotic manipulators with the torque saturationin the presence of model uncertainties and external disturbances. In comparison with the state-of-the-artobserver-based controllers in the literature, this paper introduces a saturated observer-based controllerbased on a radial basis function neural network. This technique helps the controller produce feasiblecontrol signals for the robot actuators. As a result, it efficiently diminishes the actuators saturation riskand consequently, a better transient performance is obtained. The stability analyses of the dynamicsof the tracking errors and state estimation errors are given with the help of a Lyapunov-based stabilityanalysis method. The theoretical analyses will systematically prove that the errors are semi-globallyuniformly ultimately bounded and they converge to a small set around the origin whose size is adjustableby a suitable tuning of parameters. At last, some real experiments are performed on a laboratory roboticarm to illustrate the efficiency of the proposed control system for real industrial applications.

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Journal title

International Journal of Modeling, Identification, Simulation and Control

volume 49 issue 2

pages 199- 208

publication date 2017-12-01

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Keywords

Actuator saturation Adaptive robust control Observer-based control RBF neural networks Robot manipulators

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