A modelling approach for neuro-fuzzy control of a single-link flexible robot manipulator that uses a computer-aided design (CAD) program is proposed. Initially, a CAD model of the flexible link is created using experimentally determined values of system parameters. This CAD model is then exported to MATLAB software and the Simulink/SimMechanics toolbox. An adaptive-network-based fuzzy logic controller is used for position and vibration control of the flexible link.
Experimental and simulation results are presented that validate the proposed approach.
KalyoncuM.BotsaliF. M.‘Effect of axial shortening on the vibration of elastic robot arm.’ In Proceedings of the Second Turkish-German Joint International Symposium onComputer applications, Konya, Turkey, 1998, pp. 313–324.
2.
ShiZ. X.FungE. H. K.LiY. C.‘Dynamic modeling of a rigid-flexible manipulator for constrained motion task control.’Appl. Math. Model.23 (1993): 509–525
3.
KnaniJ.‘Dynamic modeling of flexible robotic mechanisms and adaptive robust control of trajectory computer simulation-part I.’Appl. Math. Model.26 (2002): 1113–1124
4.
AnkaraliA.KalyoncuM.BotsaliF. M.ŞişmanT.‘Mathematical modeling and simulation of a flexible shaft-flexible link system with end mass.’Math. Comput. Model. Dyn. Sys.10 (3) (2004): 187–200
5.
LinJ.LewisF. L.‘A symbolic formulation of dynamic equations for a manipulator with rigid and flexible links.’Int. J. Robot. Res.13 (5) (1994): 454–466
6.
KalyoncuM.BotsaliF. M.‘Dynamic modeling of a robot manipulator with a translating and rotating elastic arm (in Turkish).’ In Proceedings of the Third International Workshop onMechatronics design and modeling, Ankara, Turkey, 1997, pp. 237–247.
7.
KalyoncuM.BotsaliF. M.‘Vibration analysis of an elastic robot manipulator with prismatic joint and a time varying end mass.’Arab. J. Sci. Engng29 (1C) (2004): 27–38
8.
DwivedyS. K.EberhardP.‘Dynamic analysis of flexible manipulators, a literature review.’Mech. Mach. Theory41 (2006): 749–777
9.
KalyoncuM.‘Mathematical modeling and dynamic response of a multi-straight-line path tracing flexible robot manipulator with rotating-prismatic joint.’Appl. Math. Model.32 (6) (2007): 1087–1098
10.
MonjeC. A.RamosF.FeliuV.VinagreB. M.‘Tip position control of a lightweight flexible manipulator using a fractional order controller.’IET Control Theory Appl.1 (5) (2007): 1451–1460
11.
SanzA.EtxebarriaV.‘Experimental control of a single-link flexible robot arm using energy shaping.’Int. J. Syst. Sci.38 (1) (2007): 61–71
12.
SubudhiB.MorrisA. S.‘Fuzzy and neuro-fuzzy approaches to control a flexible single-link manipulator.’Proc. IMechE, Part I: J. Systems and Control Engineering217 (I5) (2003): 387–400
13.
ArteagaM. A.SicilianoB.‘On tracking control of flexible robot arms.’IEEE Trans. Autom. Control45 (3) (2000): 520–527
14.
DulI.Karcz-DulI.‘Algorithms of trajectory planning with constrained deviation from a given end-effector path.’Robotica22 (2004): 633–642
15.
AtaA. A.MyoT. R.‘Optimal point-to-point trajectory tracking of redundant manipulators using generalized pattern search.’Int. J. Adva. Robot. Sys.2 (3) (2005): 239–244
16.
BurkanR.Uzmayİ.‘A model of parameter adaptive law with time varying function for robot control.’Appl. Math. Model.29 (2005): 361–371
17.
HuangP.XuY.LiangB.‘Tracking trajectory planning of space manipulator for capturing operation.’Int. J. Adv. Robot. Sys.3 (3) (2006): 211–218
18.
OwenW. S.CroftE. A.BenhabibB.‘Real-time trajectory resolution for a two-manipulator machining system.’J. Robot. Syst.22 (2006) S51-S63
Akbarzadeh-TotonchiM. R.JamshidiM.VadieeN.‘Hierarchical fuzzy controller using line-curvature feature extraction for a single link flexible arm.’ In Proceedings of the Third IEEE International Conference on, Fuzzy systems, Orlando, Florida, 26–29 June 1994, vol. 1, pp. 524–529 (IEEE, Piscataway, New Jersey).
21.
LinJ.‘Flexible link robot arm control by a hierarchical fuzzy logic approach.’ In Proceedings of the IEEE International Conference onPlasma scienceHonolulu, Hawaii, 12-17 May 2002, vol. 2, pp. 1138–1143 (IEEE, Piscataway, New Jersey).
22.
CaswaraF. M.UnbehauenH.‘A neurofuzzy approach to the control of a flexible-link manipulator.’IEEE Trans. Robot. Autom.18 (6) (2002): 932–944
23.
KuoK. Y.LinJ.‘Fuzzy logic control for flexible link robot arm by singular perturbation approach.’Appl. Soft Comput.2 (2002): 24–38
24.
LinJ.‘Hierarchical fuzzy logic controller for a flexible link robot arm performing constrained motion tasks.’IEE Proc. D, Control Theory Appl.150 (4) (2003): 355–364
25.
ChiangT. S.LinJ.‘A new design of hierarchical fuzzy hybrid position/force control for flexible link robot arm.’ In Proceedings of theAmerican control conference, Denver, Colorado, 4-6 June 2003, vol. 6, pp. 5239–5244.
26.
LinJ.LewisF. L.‘Two-time scale fuzzy logic controller of flexible link robot arm.’Fuzzy Sets Syst.139 (1) (2003): 125–149
27.
EmaraH.ElshafeiA. L.‘Robust robot control enhanced by a hierarchical adaptive fuzzy algorithm.’Engng Appl. Artif. Intell.17 (2004): 187–198
28.
LinJ.‘A vibration absorber of smart structures using adaptive networks in hierarchical fuzzy control.’J. Sound Vib.287 (4/5) (2005): 683–705
29.
JnifeneA.AndrewsW.‘Experimental study on active vibration control of a single-link flexible manipulator using tools of fuzzy logic and neural networks.’IEEE Trans. Instrum. Meas.54 (3) (2005): 1200–1208
30.
KamalasadanS.GhandaklyA. A.‘A neural network parallel adaptive controller for dynamic system control.’IEEE Trans. Instrum. Meas.56 (5) (2007): 1786–1796
31.
KarimiH. R.YazdanpanahM. J.‘A new modeling approach to single-link flexible manipulator using singular perturbation method.’Electr. Engng88 (5) (2006): 375–382
32.
KarimiH. R.YazdanpanahM. J.PatelR. V.KhorasaniK.‘Modeling and control of linear two-time scale systems: Applied to single-link flexible manipulator.’J. Intell. Robot. Syst.: Theory Appl.45 (3) (2006): 235–265
33.
LinJ.HuangZ. Z.HuangP. H.‘An active damping control of robot manipulators with oscillatory bases by singular perturbation approach.’J. Sound Vib.304 (2007): 345–360
34.
LinJ.HuangZ. Z.‘A hierarchical fuzzy approach to supervisory control of robot manipulators with oscillatory bases.’Mechatronics17 (10) (2007): 589–600
ZhangL.CaoQ.LeeJ.‘A hybrid one-by-one learning with incremental learning algorithm in CMAC.’ In Proceedings of the Fifth World Congress onIntelligent control and automation, Hangzhou. People's Republic of China, 15-19 June, 2004, pp. 2415–2418
37.
ShaD.BajicV. B.‘An on-line hybrid learning algorithm for multilayer perceptron in identification problems.’Comput. Electr. Engng28 (2002): 587–598
38.
DenaiM. A.PalisF.ZeghbibA.‘ANFIS based modeling and control of nonlinear systems: a tutorial.’ IEEE International Conference on Systems, Man and Cybernetics, 2004
39.
SiddiqueM. N. H.TokhiM. O.‘GA-based neural fuzzy control of flexible-link manipulators.’Engng Lett.13 (2) (2006): 2–13
40.
RezaeeianA.Yousefi-KomaA.ShastiB.DoosthoseiniA.‘ANFIS modeling and feedforward control of shape memory alloy actuators.’Int. J. Math. Mod. Meth. Appl. Sci.2 (2) (2008): 228–235
41.
ChopraS.MitraR.KumarV.‘A neurofuzzy learning and its application to control system.’Int. J. Comput. Intell.3 (1) (2007): 73–79
42.
BodyanskiyY.PlissI.VynokurovaO.‘Adaptive wavelet-neuro-fuzzy network in the forecasting and emulation tasks.’Int. J. Inform. Theories Appl.15 (2008): 47–55
43.
KalaichelviV.SivakumarD.KarthikeyanR.PalanikumarK.‘Prediction of the flow stress of 6061 Al-15% SiC - MMC composites using adaptive network based fuzzy inference system.’Mater. Des.30 (2009): 1362–1370
44.
DenaiM. A.PalisF.ZeghbibA.‘Modeling and control of non-linear systems using soft computing techniques.’Appl. Soft Comput.7 (2007): 728–738
45.
Gülerİ.ÜbeyliE.D., ‘Automatic detection of ophthalmic arterystenosis using the adaptive neuro-fuzzyinference system.’Engng Appl. Artif. Intell.18 (2005): 413–422
46.
KimD.‘A design of CMAC-based fuzzy logic controller with fast learning and accurate approximation.’Fuzzy Sets Syst.125 (2002): 93–104
47.
WidrowB.StearnsD.Adaptive signal processing (Prentice-Hall, Englewood Cliffs, New Jersey1985)
48.
AströmK. J.WittenmarkB.Computer controller systems: theory and design (Prentice Hall, New Jersey1984)
49.
GoodwinG. C.SinK. S.Adaptive filtering prediction and control (Prentice Hall, Englewood Cliffs, New Jersey1984)
50.
LjungL.System identification: theory for the user (Prentice Hall, Englewood Cliffs, New Jersey).1987)
51.
StrobachP.Linear prediction theory: a mathematical basis for adaptive systems (Springer-Verlag, New York1990)
52.
NarendraK. S.ParthsarathyK.‘Identification and control of dynamical systems using neural networks.’IEEE Trans. Neural Netw.1 (1) (1990): 4–27
53.
WerbosP. J.‘An overview of neural networks for control.’IEEE Control Syst. Mag.11 (1) (1991): 40–41
54.
MillerW. T.SuttonR. S.WerbosP. J.Neural networks for control (Massachusetts Institute of Technology Press, Cambridge, Massachusetts1990)
55.
WidrowB.WinterR.‘Neural nets for adaptive filtering and adaptive pattern recognition.’IEEE Comput.21 (3) (1988): 25–39
56.
KoskoB.Neural networks for signal processing (Prentice Hall, Englewood Ciffs, New Jersey1991)
57.
HaykinS. S.Adaptive filter theory, second edition 1991 (Prentice Hall, Englewood Cliffs, New Jersey).
