Under-Actuated Robot Manipulator Positioning Control Using Artificial Neural Network Inversion Technique

Ali T Hasan

doi:10.1155/2012/927905

Abstract

This paper is devoted to solve the positioning control problem of underactuated robot manipulator. Artificial Neural Networks Inversion technique was used where a network represents the forward dynamics of the system trained to learn the position of the passive joint over the working space of a 2R underactuated robot. The obtained weights from the learning process were fixed, and the network was inverted to represent the inverse dynamics of the system and then used in the estimation phase to estimate the position of the passive joint for a new set of data the network was not previously trained for. Data used in this research are recorded experimentally from sensors fixed on the robot joints in order to overcome whichever uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in gear trains. Results were verified experimentally to show the success of the proposed control strategy.

Highlights

Underactuated robot manipulator possesses fewer actuators than degrees of freedom (DOF)
Artificial neural networks (ANN) technique has gained a great deal of interest for their extreme flexibility due to its learning ability and the capability of non-linear function approximation
We proposed to introduce the network inversion technique to the problem of underactuated robot control that estimating the input parameters necessary to Angular position

Summary

Introduction

Underactuated robot manipulator possesses fewer actuators than degrees of freedom (DOF). Nonholonomic behavior, and lack of feedback linearizability are often exhibited in such systems, making that class of robots a challenging one for synthesis of control schemes Due to their advantages over fully actuated robots, this type of manipulators has gained the interest of several researchers [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]. Artificial neural networks (ANN) technique has gained a great deal of interest for their extreme flexibility due to its learning ability and the capability of non-linear function approximation They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems, and, once trained, can perform prediction and generalization at high speed. The efficiency of the proposed method is shown experimentally using 2R underactuated robot manipulator

The Robot System

Network Inversion Technique

ANN Implementation

Conclusions