Abstract
A robust tracking controller for robot manipulators measuring only the angular positions and considering model uncertainties is presented. It is considered that the model is uncertain; that is, the system parameters, nonlinear terms, external perturbations, and the friction effects in each robot joint are considered unknown. The controller is composed by two parts, a linearizing-like control feedback and a high-gain estimator. The main idea is to lump the uncertain terms into a new state which represents the dynamics of the uncertainties. This new state is then estimated in order to be compensated. In this way the resulting controller is robust. A numerical example for a RR robot manipulator is provided, in order to corroborate the results.
Highlights
Control of manipulators is a classical control problem [1,2,3,4], due to the innumerable applications, for instance, in manufacturing processes, biomedical engineering, and aeronautical
To corroborate the performance of the robust controller in the effects of parameter variations, modeling errors, and external perturbations, we propose to apply the robust controller to a RR manipulator
In this work we present a robust tracking control for the compensation of modeling errors, parameter variations, and external perturbation for a class of robot manipulators
Summary
Control of manipulators is a classical control problem [1,2,3,4], due to the innumerable applications, for instance, in manufacturing processes, biomedical engineering, and aeronautical. The present contribution consists in considering uncertain the parameters of the manipulator and the effects of the friction; it is not necessary to estimate or to adapt to any parameter In this sense, the controller is considered robust against parameter variations, nonmodeled dynamics (friction dynamics), and external disturbances. In the present contribution a controller which compensate friction and modeling errors is described In this sense, the Modelling and Simulation in Engineering controller is capable to compensate modeling errors, friction, parameter variations, and external perturbations using only the measure of the angular position of the robots. The linearizing-like controller requires the knowledge of such an unknown state To tackle this problem, we use a high-gain state estimator to obtain an estimated value for this uncertain term [9]. The paper is organized as follows: in Section 2, the dynamic model for a robot manipulator is presented, Section 3 contains the main contribution on nonlinear robust control, Section 4 presents the simulation results, and in Section 5, some conclusions are given
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