Olaf Wandel

Robot Control using an Artificial Neural Network: An Individual Undergraduate Project Report Olaf Wandel

Diplomarbeit, die am 31.05.1997 erfolgreich an einer Fachhochschule in Deutschland im Fachbereich Allgemeiner Maschinenbau eingereicht wurde. Abstract: The aim of the project was to control three joints of an industrial robot in terms of its position, velocity and acceleration. The work considered the necessary hardware, principles of neural networks and controlling techniques. The hardware comprised of a robot with three DC-motors and three optical position encoders, a personal computer with a D/A card for voltage output to the robot and two D/D cards. One D/D card for receiving values from the optical encoders and one for timing. The basics of artificial neural network type perceptrons were described. The special features bias, output feedback, momentum term, adjustment of momentum factor and adjustment of learning rate for this artificial neural network type were considered. An introduction to learning and control structures using artificial neural networks were given. These were controller copying, direct modelling, direct inverse modelling, control with a model and an inverse model, forward and inverse modelling, control action feedback error learning, feedback error learning, learning and control using the plant's Jacobian. The conversion of two learning and control structures, direct inverse modelling and control action feedback error learning, was implemented in software using "MS QuickBASIC 4.5". One joint was controlled with a direct inverse model. One joint and all joints together were controlled with control action feedback error learning. The results of experiments with these learning and control structures were documented. Table of Contents: 1.|Introduction|8 2.|The hardware|9 2.1|The robot|9 2.2|The computer and the software|11 2.3|The PCL-726 D/A card|11 2.4|The D/D card|11 2.5|The PCL-812 D/D card|12 2.6|The G64 rack|12 3.|Neural networks|13 3.1|The neuron|13 3.2|Conversion of neural networks|14 3.3|Learning