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

Pourrahim, M.; Shojaei, K.; Chatraei, A.; Shahnazari, O.

doi:10.22060/miscj.2017.12177.5010

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

Document Type : Research Article

Authors

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

10.22060/miscj.2017.12177.5010

Abstract

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

Highlights

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Keywords

References