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Taschenbuch. Etat : Neu. Robot Control using an Artificial Neural Network | An Individual Undergraduate Project Report | Olaf Wandel | Taschenbuch | 60 S. | Englisch | 2001 | [.] | EAN 9783838641416 | Verantwortliche Person für die EU: Bedey und Thoms Media GmbH, Hermannstal 119k, 22119 Hamburg, kontakt[at]bedey-media[dot]de | Anbieter: preigu. …

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Diploma Thesis from the year 1997 in the subject Engineering - Mechanical Engineering, grade: 1,0, Hamburg University of Applied Sciences (Allgemeiner Maschinenbau), language: English, abstract: Inhaltsangabe: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.Inhaltsverzeichnis:Table of Contents:1.Introduction82.The hardware92.1The robot92.2The computer and the software112.3The PCL-726 D/A card112.4The D/D card112.5The PCL-812 D/D card122.6The G64 rack123.Neural networks133.1The neuron133.2Conversion of neural networks143.3Learning principles of neural networks173.4Special modifications to the neural network used193.5Learning capacity224.Teaching and control techniques235.The software285.1The teaching and control program285.1.1The direct inverse modelling and trajectory estimation program295.1.2The control action feedback error learning program306.Experiments with learning and control structures316.1Direct inverse modelling316.1.1Direct inverse modelling of the waist326.1.2Trajectory estimation for the waist346.2Control action feedback error learning (CAFEL)366.2.1Control action feedback error learning of the waist376.2.2Control action feedback error learning of the shoulder396.2.3Control action feedback error learning of all joints at the same time397.Conclusions and recommendations448.Appendix478.1Symbols478.2Indices488.3Bibliography498.4Floppy disk with the program508.5Floppy disks with this report50 60 pp. Englisch. …

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Diploma Thesis from the year 1997 in the subject Engineering - Mechanical Engineering, grade: 1,0, Hamburg University of Applied Sciences (Allgemeiner Maschinenbau), language: English, abstract: Inhaltsangabe: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.Inhaltsverzeichnis:Table of Contents:1.Introduction82.The hardware92.1The robot92.2The computer and the software112.3The PCL-726 D/A card112.4The D/D card112.5The PCL-812 D/D card122.6The G64 rack123.Neural networks133.1The neuron133.2Conversion of neural networks143.3Learning principles of neural networks173.4Special modifications to the neural network used193.5Learning capacity224.Teaching and control techniques235.The software285.1The teaching and control program285.1.1The direct inverse modelling and trajectory estimation program295.1.2The control action feedback error learning program306.Experiments with learning and control structures316.1Direct inverse modelling316.1.1Direct inverse modelling of the waist326.1.2Trajectory estimation for the waist346.2Control action feedback error learning (CAFEL)366.2.1Control action feedback error learning of the waist376.2.2Control action feedback error learning of the shoulder396.2.3Control action feedback error learning of all joints at the same time397.Conclusions and recommendations448.Appendix478.1Symbols478.2Indices488.3Bibliography498.4Floppy disk with the program508.5Floppy disks with this report50. …

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Diploma Thesis from the year 1997 in the subject Engineering - Mechanical Engineering, grade: 1,0, Hamburg University of Applied Sciences (Allgemeiner Maschinenbau), language: English, abstract: Inhaltsangabe:Abstract:The aim of the project was to con. …

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Inhaltsangabe: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.Inhaltsverzeichnis:Table of Contents:1.Introduction82.The hardware92.1The robot92.2The computer and the software112.3The PCL-726 D/A card112.4The D/D card112.5The PCL-812 D/D card122.6The G64 rack123.Neural networks133.1The neuron133.2Conversion of neural networks143.3Learning principles of neural networks173.4Special modifications to the neural network used193.5Learning capacity224.Teaching and control techniques235.The software285.1The teaching and control program285.1.1The direct inverse modelling and trajectory estimation program295.1.2The control action feedback error learning program306.Experiments with learning and control structures316.1Direct inverse modelling316.1.1Direct inverse modelling of the waist326.1.2Trajectory estimation for the waist346.2Control action feedback error learning (CAFEL)366.2.1Control action feedback error learning of the waist376.2.2Control action [¿]Diplomica Verlag, Hermannstal 119k, 22119 Hamburg 60 pp. Englisch.…