force velocity control with neural network compensation for contour tracking with pneumatic actuation

235 Pages·2008·3.69 MB·English

FORCE VELOCITY CONTROL WITH NEURAL NETWORK
COMPENSATION FOR CONTOUR TRACKING WITH
PNEUMATIC ACTUATION

BY

MOHAMMED ABU MALLOUH

A thesis submitted to the Department of Mechanical and Materials Engineering in
conformity with the requirements for the degree of Doctor of Philosophy

Queen’s University
Kingston, Ontario, Canada
September, 2008

Copyright © Mohammed Abu Mallouh, 2008
ABSTRACT
Mohammed Abu Mallouh: Force Velocity Control with Neural Network Compensation for
Contour Tracking with Pneumatic Actuation. Ph.D. Thesis, Queen’s University, August, 2008.

Control of the contact force between a robot manipulator and a workpiece is critical for
successful execution of tasks where the robot’s end effector must perform a contact operation
along the contour of a workpiece. Representative tasks include polishing, grinding and deburring.
Considerable research has been conducted on force control with electric robots. By contrast, little
research has been conducted on force control with pneumatic robots. The later has the potential
to be considerably cheaper. However, the compressible nature of air as the working fluid and
relatively high friction means pneumatic robots are more difficult to control. The subject of this
thesis is the design and testing of a controller that regulates the normal contact force and
tangential velocity of the end effector of a pneumatic gantry robot while tracking the contour of a
planar workpiece. Both experimental and simulation results are presented.

A PI Force Velocity (FV) controller for contour tracking was designed and tested experimentally.
Three different workpiece edge geometries were studied: straight, inclined and curved. The
tracking performance with the PI FV controller was comparable to the performance reported by
other researchers with a similar controller implemented with an electric robot. This result
confirms the potential of pneumatically actuated robots in force control applications.

A system model was developed and validated in order to investigate the parameters that affect
performance. A good match between experiment and simulation was achieved when the friction
of the z-axis cylinder was modeled with a Displacement Dependent Friction Model (DDFM)
instead of a Velocity Dependent Friction Model (VDFM). Subsequently, a DDFM based friction
compensator was designed and tested. However, it was found that performance could not be
improved even with perfect friction compensation, due to the effects of system lag.

Two Neural Network (NN) compensators were designed to compensate for both the lag and
friction in the system. Simulation results for straight and curved edges were used to examine the
effectiveness of the NN compensators. The performance of the PI FV controller was found to
improve significantly when a NN compensator was added. This result confirms the value of
NN’s in control compensation for tracking applications with pneumatic actuation.
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ACKNOWLEDGEMENTS
With a deep sense of gratitude, I wish to express my sincere thanks to my supervisor, Dr. Brian
Surgenor, for his motivation and supervision through out the course of this research.

My sincere thanks are due to Dr. Ron Anderson, for the help extended to me when I approached
him and the valuable discussion that I had with him during the course of my study at Queen’s
University. I will neglect my duty if I do not mention my fellow research scholars, the laboratory
staff and administrative staff of the Mechanical and Materials Engineering Department for their
timely help, with special thanks to Mr. O. Oosten and Mr. A. McPhail for their quick and great
help with my experimental setup.

The financial support provided by the Hashemite University and Queen’s University is greatly
acknowledged.

I also want to thank my parents, who taught me the value of hard work by their own example.
They rendered me enormous support during the whole tenure of my research. Also special thanks
to my father and mother in law for their support.

Lastly, I am grateful to my wife Kholoud for the inspiration and moral support she provided
throughout my research work. Without her loving support and understanding I would never have
completed my present work. Particularly, I owe to my sons Karam and Yazan for their silent
prayer for my work at the time when they needed my company most.

Finally, I would like to thank all whose direct and indirect support helped me complete my thesis.
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TABLE OF CONTENTS
ABSTRACT.....................................................................................................................................ii

ACKNOWLEDGEMENTS............................................................................................................iii
TABLE OF CONTENTS................................................................................................................iv
LIST OF FIGURES......................................................................................................................viii
LIST OF TABLES.........................................................................................................................xv
LIST OF NOMENCLATURE.....................................................................................................xvii

Chapter 1 INTRODUCTION...........................................................................................................1
1.1 Background and Motivation..................................................................................................2
1.2 Objectives..............................................................................................................................4
1.3 Contributions.........................................................................................................................4
1.4 Thesis Outline........................................................................................................................5
Chapter 2 LITERATURE REVIEW................................................................................................7
2.1 Force Control for Robots.......................................................................................................7
2.2 Force Control for Pneumatic Robots...................................................................................12
2.3 Contour Tracking.................................................................................................................20
2.4 Neural Networks..................................................................................................................21
2.4.1 Neural Networks in Control..........................................................................................23
2.5 Fuzzy Logic Systems...........................................................................................................25
2.5.1 Fuzzy Logic in Control.................................................................................................26
2.6 Neural Networks and Fuzzy Logic in Control.....................................................................28
2.6.1 Adaptive Neuro Fuzzy Inference System.....................................................................28
2.7 Friction and Friction Compensation....................................................................................31
2.7.1 Friction Models.............................................................................................................31
2.7.2 Friction Compensation..................................................................................................32
2.8 Summary..............................................................................................................................36
Chapter 3 EXPERIMENTAL CONTROLLER DESIGN.............................................................38
3.1 Apparatus Description.........................................................................................................38
3.1.1 Pneumatic Cylinders.....................................................................................................40
3.1.2 Valves...........................................................................................................................41
3.1.3 Sensors..........................................................................................................................41
3.1.4 Data Acquisition and Control.......................................................................................42
3.2 Noise in Measured Signals...................................................................................................44
3.3 Jatta and Benchmark FV Controller.....................................................................................46
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3.4 Designing the PID FV Controller........................................................................................51
3.4.1 Tuning Step 1................................................................................................................53
3.4.2 Tuning Step 2................................................................................................................54
3.4.3 Tuning Step 3................................................................................................................57
3.5 Effect of System Parameters................................................................................................59
3.5.1 Effect of Surface Finish................................................................................................59
3.5.2 Effect of Profile Shape and Angle................................................................................62
3.5.3 Effect of Tracking Speed..............................................................................................64
3.6 Retuned PI FV Controller....................................................................................................68
3.6.1 Benchmark Results with Retuned Controller................................................................68
3.7 PI FV Controller with VDFM based Compensator..............................................................74
3.8 Summary..............................................................................................................................76
Chapter 4 MODELING AND SIMULATION RESULTS............................................................77
4.1 Dynamic Modeling..............................................................................................................77
4.1.1 General Dynamic Modeling..........................................................................................77
4.1.2 Contact Modeling.........................................................................................................79
4.1.3 System Lag Modeling...................................................................................................80
4.1.4 Signal Noise Modeling.................................................................................................84
4.1.5 Workpiece Surface Finish Modeling............................................................................86
4.2 Model Validation.................................................................................................................87
4.2.1 Validation Stage 1.........................................................................................................87
4.2.2 Displacement Dependent Friction Model.....................................................................90
4.2.3 Modeling and Validating the DDFM............................................................................94
4.2.4 ANFIS DDFM..............................................................................................................96
4.2.5 Validation Stage 2.........................................................................................................98
4.3 Effect of System Parameters..............................................................................................107
4.3.1 Effect of Unidirectional PPC Valves..........................................................................107
4.3.2 Effect of Tracking Speed............................................................................................109
4.3.3 Effect of Uncertainty in Workpiece Angle.................................................................110
4.3.4 Effect of Signal Measurement Noise..........................................................................111
4.3.5 Effect of System Lag..................................................................................................111
4.3.6 Effect of Workpiece Surface Finish............................................................................112
4.3.7 Effect of Coulomb Friction.........................................................................................112
4.3.8 Effect of Workpiece Shape.........................................................................................114
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4.4 Workpiece Surface Finish Effect.......................................................................................114
4.4.1 Straight Edge Tracking...............................................................................................115
4.4.2 Inclined Edge Tracking...............................................................................................117
4.4.3 Curved Edge Tracking................................................................................................119
4.5 Summary............................................................................................................................121
Chapter 5 NEURAL NETWORK BASED COMPENSATION.................................................123
5.1 PI FV Controller with Gain Scheduler...............................................................................123
5.2 PI FV Controller with Friction Compensation...................................................................126
5.3 System Lag Compensation................................................................................................128
5.4 PI FV Controller with Neural Network Compensation......................................................132
5.4.1 EBPA Neural Network Compensator.........................................................................134
5.4.2 MBPM Neural Network Compensator.......................................................................136
5.4.3 Results with PI FV Controller and NN Compensation...............................................138
5.5 Summary............................................................................................................................146
Chapter 6 CONCLUSIONS AND RECOMMENDATIONS......................................................148
6.1 Conclusions........................................................................................................................148
6.1.1 Summary Results........................................................................................................149
6.1.2 Experimental Controller Design.................................................................................151
6.1.3 Modeling and Simulation Results...............................................................................152
6.1.4 Neural Network Based Compensation........................................................................153
6.2 Recommendations..............................................................................................................155
References....................................................................................................................................157

Appendix A Technical Specifications..........................................................................................164
A.1 SMC VER PPC Valve.......................................................................................................164
A.2 SMC MY1H Cylinder.......................................................................................................165
A.3 Bimba Standard Cylinder..................................................................................................166
A.4 SMC VEA 250 Power Amplifier......................................................................................167
A.5 FUTEK Bi-Axial Force Sensor.........................................................................................168
A.6 FUTEK CSG (JM-2A) Amplifier.....................................................................................169
A.7 Celesco Position and Velocity Transducer.......................................................................170
A.8 Omega Differential Pressure Sensor.................................................................................171
A.9 DMD-465WB Signal Conditioner....................................................................................172
A.10 dSPACE DS1104 R&D Controller Board......................................................................173
A.11 SKF Ball Bearing............................................................................................................174
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Appendix B Sensor Calibration...................................................................................................175
B.1 Force Sensor Calibration...................................................................................................175
B.2 Position Sensor Calibration...............................................................................................177
B.3 Velocity Sensor Calibration..............................................................................................179
B.4 Differential Pressure Sensor Calibration...........................................................................186
Appendix C System Parameter Identification..............................................................................188
C.1 Friction Parameter Identification.......................................................................................188
C.1.1 Coulomb Friction Identification.................................................................................188
C.1.2 Viscous Friction Coefficient Identification................................................................197
C.2 System Lag Identification.................................................................................................198
Appendix D Neural Network Program Details............................................................................201
D.1 Neural Network Compensation with 14 Inputs.................................................................201
D.2 Neural Network Compensation with 5 Inputs...................................................................206
Appendix E Supplemental Tuning Results..................................................................................208
E.1 Tuning the x-axis Velocity Controller...............................................................................208
E.2 Tuning the z-axis Force Controller....................................................................................209
E.3 Tuning the x-axis Force Controller....................................................................................210
E.4 Tuning the z-axis Velocity Controller...............................................................................212
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LIST OF FIGURES
Figure 1.1: Contour tracking of a planar glass workpiece during grinding process........................1
Figure 1.2: Contour tracking of an engine part during polishing process........................................2
Figure 2.1: End effector design for passive stiffness control...........................................................8
Figure 2.2: Impedance force control................................................................................................9
Figure 2.3: Block diagram of FP controller...................................................................................10
Figure 2.4: Block diagram of parallel FP controller......................................................................11
Figure 2.5: Tracking performance with short tubes (a) full order (b) reduced order.....................15
Figure 2.6: Tracking performance with long tubes (a) full order (b) reduced order......................15
Figure 2.7: Frequency response, full and reduced order, short tube..............................................16
Figure 2.8: Frequency response, full and reduced order, long tube...............................................17
Figure 2.9: Force tracking with hybrid ANFMRC........................................................................18
Figure 2.10: Labeled generic neuron.............................................................................................21
Figure 2.11: General mathematical neuron model.........................................................................22
Figure 2.12: Some common choices for the activation function...................................................22
Figure 2.13: Multiple layers of neurons.........................................................................................23
Figure 2.14: NN model reference control......................................................................................24
Figure 2.15: Feedforward with NN inverse model control............................................................24
Figure 2.16: NN internal model based control...............................................................................25
Figure 2.17: NN generalized predictive control.............................................................................25
Figure 2.18: Generic Fuzzy Logic system.....................................................................................26
Figure 2.19: Step response of a second order system with 3 different controllers........................28
Figure 2.20: (a) Sugeno’s FL system and (b) equivalent ANFIS..................................................29
Figure 2.21: Friction models (a) Coulomb, (b) Coulomb plus viscous, (c) nonlinear, (d)
exponential.....................................................................................................................................32
Figure 2.22: Selection of friction compensation techniques..........................................................33
Figure 2.23: Neuro-fuzzy friction compensator architecture.........................................................35
Figure 2.24: Experimental and NN estimated friction...................................................................35
Figure 3.1: Pneumatic gantry robot with main components labeled..............................................39
Figure 3.2: Close up of force sensor showing bearing in contact with edge of workpiece............40
Figure 3.3: Profile of the aluminum workpiece used in producing benchmark results.................40
Figure 3.4: A typical pneumatic system diagram with PPC valves...............................................41
Figure 3.5: Illustration of noise level in the measured signals.......................................................45
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Figure 3.6: Block diagram of the Dual Force Velocity controller.................................................46
Figure 3.7: f tracking response for iron rectangular edge workpiece..........................................47
n
Figure 3.8: f tracking response for wooden curved edge workpiece..........................................47
n
Figure 3.9: f tracking response for iron circular edge workpiece..............................................48
n
Figure 3.10: Adaptive neuro-fuzzy FV controller plus neuro-fuzzy friction compensator...........50
Figure 3.11: Block diagram PI FV controller with VDFM based compensator............................50
Figure 3.12: Representation of the gantry robot operating on a planar curved workpiece............51
Figure 3.13: Block diagram of FV controller with four PID elements shown...............................52
Figure 3.14: Results of the PID velocity control of x-axis when Kvx was changed (example of
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Step 1 tuning).................................................................................................................................54
Figure 3.15: Responses when tracking straight edge wood workpiece with z-axis force controller
combined with the x-axis velocity controller (example of Step 2 tuning).....................................56
Figure 3.16: Responses when tracking straight edge wood workpiece with z-axis velocity
controller combined with the x-axis force controller (example of Step 2 tuning).........................56
Figure 3.17: f and v responses when tracking wood workpiece with straight edge profile
n t
(example of Step 3 tuning).............................................................................................................59
Figure 3.18: Surface roughness and surface straightness..............................................................61
Figure 3.19: f response when tracking wood workpiece with straight edge profile...................61
n
Figure 3.20: f response when tracking steel workpiece with straight edge profile....................61
n
Figure 3.21: f response when tracking aluminum workpiece with straight edge profile...........62
n
Figure 3.22: f response when tracking wood workpiece with θ= +3 deg.................................63
n
Figure 3.23: f response when tracking wood workpiece with θ= -10 deg................................63
n
Figure 3.24: f response when tracking wood workpiece with θ= +92 deg...............................63
n
Figure 3.25: f response when tracking steel workpiece with curved edge.................................64
n
Figure 3.26: f and v responses when tracking aluminum curved edge, with medium v ........66
n t td
Figure 3.27: f and v responses when tracking aluminum curved edge, with low v ...............66
n t td
Figure 3.28: f and v responses when tracking aluminum straight edge, with medium v .......67
n t td
Figure 3.29: f and v responses when tracking aluminum straight edge, with low v ..............67
n t td
Figure 3.30: Responses when tracking aluminum straight edge (θ = zero deg)...........................70
Figure 3.31: Responses when tracking aluminum inclined edge (θ = 7 deg)...............................71
Figure 3.32: Responses when tracking aluminum inclined edge (θ = 16 deg).............................72
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Figure 3.33: Responses when tracking aluminum curved profile edge.........................................73
Figure 3.34: Responses with PI only & PI with VDFM compensator, aluminum straight edge...75
Figure 4.1: Experimental result of m identification test.............................................................79
x
Figure 4.2: Modeling the contact...................................................................................................80
Figure 4.3: Sample τ identification test, when piston held still at x =1000 mm..........................83
x
Figure 4.4: Sample τ identification test, when piston held still at z = 100 mm...........................83
z
Figure 4.5: NDRNG noise added to θ..........................................................................................84
Figure 4.6: Signals with the noise model added............................................................................85
Figure 4.7: θ with the NDRNG signal been added to model surface finish, for straight edge.....86
Figure 4.8: x-axis simulation result for PI velocity tuning, responses when KVx = 0.5 and KVx =
P I
[8.0, 4.0, 2.0]..................................................................................................................................88
Figure 4.9: x-axis experimental result for PI velocity tuning, responses when KVx = 0.5 and KVx
P I
= [8.0, 4.0, 2.0]...............................................................................................................................88
Figure 4.10: z-axis simulation result for PI force tuning, responses when KFz = 0.02 and KFz =
P I
[0.32, 0.16, 0.08]............................................................................................................................89
Figure 4.11: z-axis experimental result for PI force tuning, responses when KFz = 0.02 and KFz
P I
= [0.32, 0.16, 0.08].........................................................................................................................89
Figure 4.12: DDFM (a) Dahl model, (b) Bowden and Tabor model.............................................91
Figure 4.13: Experimental open loop result for z axis DDFM identification test (with wire force)
.......................................................................................................................................................92
Figure 4.14: Hysterisis loop of the z-axis DDFM (with wire force)..............................................93
Figure 4.15: Experimental open loop result for the z axis DDFM identification test (without wire
force)..............................................................................................................................................93
Figure 4.16: Experiment and simulation open loop results for z-axis DDFM identification.........94
Figure 4.17: Experimental and simulation hysterisis loop of the z-axis DDFM............................95
Figure 4.18: Experimental and simulation result for z-axis PI stationary force control, responses
when KFz = 0.02 and KFz = 0.08................................................................................................96
P I
Figure 4.19: ANFIS DDFM for z-axis cylinder.............................................................................97
Figure 4.20: Friction results of the experimental open loop test, the rule based DDFM and the
ANFIS DDFM (off-line)................................................................................................................98
Figure 4.21: SIMULINK block diagram for PI FV controller, gantry robot and workpiece.........99
Figure 4.22: Simulation responses when tracking straight edge (θ = zero deg.)........................101
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