The control problem of a flexible robotic arm has been investigated. The control strategies that have been developed have a wide application in approaching the general control problem of flexible space structures. The following control strategies have been developed and evaluated: neural self-tuning control algorithm, neural-network-based fuzzy logic control algorithm, and adaptive pole assignment algorithm. All of the above algorithms have been tested through computer simulation. In addition, the hardware implementation of a computer control system that controls the tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical shaft of a dc motor, has been developed. An adaptive pole assignment algorithm has been applied to suppress vibrations of the described physical model of flexible robotic arm and has been successfully tested using this testbed

Bialasiewicz, Jan T.

English

NASA Technical Reports Server

NASA-CR-200258
ADAPTIVE CONTROL STRATEGIES
FOR FLEXIBLE ROBOTIC ARM
_/ _/_7
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FINAL REPORT
Principal Investigator: Dr. Jan T. Bialasiewicz
University of Colorado at Denver
Department of Electrical Engineering
Campus Box 110
P.O. Box 173364
Denver, Colorado 80217-3364
Period covered by the report: July 15, 1992 to January 31, 1996
Grant Number: NAG- 1- 1444
https://ntrs.nasa.gov/search.jsp?R=19960015558 2020-06-16T04:47:42+00:00Z
1 ABSTRACT
Adaptive Control Strategies for Flexible Robotic Arm
The control problem of a flexible robotic arm has been investigated. The control
strategies that have been developed have a wide application in approaching the general
control problem of flexible space structures. The following control strategies have been
developed and evaluated:
• Neural Self-Tuning Control Algorithm
• Neural-Network-Based Fuzzy Logic Control Algorithm
• Adaptive Pole Assignment Algorithm.
All of the above algorithms have been tested through computer simulation.
In addition, the hardware implementation of a computer control system that controls the
tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical
shaft of a dc motor, has been developed.
An Adaptive Pole Assignment Algorithm has been applied to suppress vibrations of the
described physical model of flexible robotic arm and has been successfully tested using
this testbed.
SUMMARY OF THE PROJECT
This research commenced on July 15, 1992 and has been supported by the Research
Grant No. NAG-l-1444. Three Graduate Research Assistants have been working on the
project at 1/3 time during its various phases. The results obtained have been discussed in
three Master's Theses and two papers presented at international conferences and
published in conference proceedings. One of the Graduate Research Assistants and the
Principal Investigator have also presented a seminar at the NASA Langley Research
Center.
At the beginning of this research, a neural network direct adaptive control schemes have
been applied to control the tip position of a flexible robotic arm. However, satisfactory
control performance was not attainable due to the inherent non-minimum phase
characteristics of the flexible robotic arm system. Therefore, a neural self-tuning control
(NSTC) algorithm has been developed and applied. This approach has shown promising
results. Simulation results of the NSTC scheme and the conventional self-tuning control
scheme have been used to examine performance factors such as control tracking mean
square error, estimation mean square error, transient response, and steady-state response.
The results of this part of the research have been presented in reference [1] and have
been reported in [2], a paper presented at the international conference Artificial Neural
Networks in Engineering ANNIE '93.
In the secondhalf of this research,two graduatestudentshavebeenworking underthe
supervisionof the principal investigator.Oneof themhasjoined the projectto develop
thelaboratoryset-up,includinghardware-softwareinterface,to enabletheevaluationand
necessarymodificationsor the algorithmsdeveloped,as well as to develop, test, and
modify otheradaptivestrategies.Theotherstudent,hasbeenworking on the adaptive
neuralnetwork-basedti_zzylogic controlalgorithms. It wasoriginally intendedto verify
theresultsof his researchusingthelaboratorysetup. However,thedelaysin acquisition
of all necessaryhardwareelementsand in developmentof the necessarysoftware,made
this goal impossiblewithin the time-frameof his thesisresearch.He finally testedhis
algorithmsthroughcomputersimulation.Theresultsof his research avebeenpresented
in reference[3] and have beenreportedin [4], a paperpresentedat the international
conferenceArtificial NeuralNetworksin EngineeringANNIE '94.
Finally, thedevelopmentof the laboratorymodelof flexible roboticarmcomputercontrol
systemhasbeencompletedby the third graduatestudentandhehassuccessfullytested
the adaptivepole assignmentalgorithmdevelopedto suppressvibrationsof thetip of a
flexible arm. This laboratorysetup,whichbesidesaphysicalmodelof a flexible robotic
arm and a computerwith hardware-softwareinterfaceincludesanalogoptical position
indicator,wide anglelensandLED source with high intensity output, has also been used
to perform identification of tile flexible manipulator. The identification was carried out
using two methods: the Observer/Kalman Filter algorithm and the recursive least squares
algorithm. The identified model was then used in the design of a dead-beat controller
which was relatively successful in controlling the flexible manipulator. The results of
this part of the research, including a detailed description of the laboratory setup, are
presented in [5].
The abstracts of all referenced reports/publications are included in the Appendix.
3 REFERENCES
[1] Ho, Long T., "Neural Sell-Tuning Adaptive Control of Non-Minimum Phase System
Developed for Flexible Robotic Arm", M.S. Thesis, Department of Electrical
Engineering, University of Colorado at Denver, 1994.
[2] Ho, Long T. and Jan T. Bialasiewicz, "Neural Self-Tuning Adaptive Control of Non-
Minimum Phase System", in Intelligent Engineering Systems Through Artificial
Neural Networks', Vol. 3, ASME Press,1993, pp. 575-580.
[3] Leung, Kam L., "Learning Fuzzy Logic Control System", M.S. Thesis, Department
of Electrical Engineering, University of Colorado at Denver, 1994.
[4] Leung, Kam L. and Jan T. Bialasiewicz, "Performance Evaluation of a Learning
Fuzzy Logic Control System", in Intelligent Engineering Systems Through Artificial
Neural Networks', Vol. 4, ASME Press,1994, pp. 277-282.
[5] Kebede Anteneh, "Identification and Control of a Flexible Robotic Arm Model",
M.S. Thesis, Department of Electrical Engineering, University of Colorado at
Denver, 1994.
APPENDIX
ABSTRACTS
Ho, Long T., "Neural Self-Tuning Adaptive Control of Non-Minimum Phase System
Developed for Flexible Robotic Arm", M.S. Thesis, Department of Electrical
Engineering, University of Colorado at Denver, 1994.
The motivation of this research came about when a neural network direct adaptive control
schemes were applied to control the tip position of a flexible robotic ann. Satisfactory
performance was not attainable due to the inherent non-minimum phase characteristics of
the flexible robotic arm. Most of the existing neural network control algorithms are
based on the direct method and exhibit very high sensitivity if not unstable closed loop
behavior. Therefore, a neural self-tuning control (NSTC) algorithm has been developed
and applied to this problem and showed promising results. Simulation results of the
NSTC scheme and the conventional self-tuning control scheme are used to examine
performance factors such as control tracking mean square error, estimation mean square
error, transient response, and steady-state response.
Ho, Long T. and Jan T. Bialasiewicz, "Neural Self-Tuning Adaptive Control of Non-
Minimum Phase System", in Intelligent Engineering Systems Through Artificial
Neural Networks, Vol. 3, ASME Press,1993, pp. 575-580.
A neural network adaptive control schemes have been applied to control a tip position of
a flexible robotic arm. It has been shown that the neural self-tuning adaptive control
algorithm has a very comparable performance to the conventional self-tuning control
algorithm. Unlike other applications of neural networks, where thousands of iterations
were required before the network can be maturely trained, in this application the neural
network identification had a convergence rate comparable to that of the recursive least
squares.
Leung, Kam L., "Learning Fuzzy Logic Control System", M.S. Thesis, Department
of Electrical Engineering, University of Colorado at Denver, 1994.
The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in
this thesis, has been evaluated. The learning Fuzzy Logic Controller (LFLC) learns to
control the motor by learning the set of teaching values that are generated by a classical
PI controller. It is assumed that the classical PI controller is tuned to minimize the error
of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller
developed in this thesis is a multi-input single-output network. Training of the LFLC is
implemented off-line. Upon completion of the training process, the FLFC replaces the
classical PI controller. In this thesis, a closed-loop position control system of a D.C.
motor using the LFLC is implemented. The primary focus is on the learning capabilities
of the LFLC. The learning includes symbolic representation of the Input Linguistic
Nodes set and the Output Linguistic Nodes set. In addition, we investigate the
knowledge-based representation of the network. As part of the design process, we
implement a digital computer simulation of the LFLCS. The computer simulation
program is written in "C" language, and it is implemented in DOS plattbrm. The LFLCS,
designed in this thesis, has been developed on an IBM compatible 486-DX2 66 computer.
First, the performance of the LFLC is evaluated by comparing the angular shaft position
of the D.C. motor controlled by' a conventional PI controller and that controlled by the
LFLC. Second, the symbolic representation of the LFLC and the knowledge-based
representation for the network arc investigated by observing the parameters of the Fuzzy
Logic membership functions and the links at each layer of the LFLC. While there are
some limitations of application with this approach, the result of the simulation shows that
the LFLC is able to control the angular shaft position of the D.C. motor. Furthermore,
the LFLC has better performance in rise time, settling time and steady-state error than the
conventional PI controller.
Leung, Kam L. and Jan T. Bialasiewicz, "Performance Evaluation of a Learning
Fuzzy Logic Control System". in Intelligent Engineering <S_vstems Through Arti/icial
Neural Networks, Vol. 4, ASME Press,1994, pp. 277-282.
The performance of a Fuzzy, l_ogic Control System has been evaluated in this paper. In
this system the fuzzy logic controller controls the shaft position of a D.C. motor. The
teaching pattern is generated by the PID controller properly tuned to give the acceptable
perfornmnce of the position control system of the D.C. motor.
Kebede Anteneh, "Identification and Control of a F'lexible Robotic Arm Model",
M.S. Thesis, Department of Electrical Engineering, University of Colorado at
Denver, 1994.
The purpose of this thesis is to design a controller which can suppress vibrations of a
physical model of flexible robotic arm. As part of the design, the identification of the
flexible robotic arm model was carried out using the observer/Kalman filter identification
and the recursive least squares algorithms. An adaptive pole placement controller is used
to control the flexible robotic arm model. A gradient estimator is used to eliminate the
possible bursting phenomena in the adaptive control system.


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