2017
49
2
0
134
NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self Organizing Map
2
2
Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic selforganizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different strategies of Game Theory are proposed to provide a competitive game for nonwinningneurons to participate in the learning phase and obtain more input patterns. The performanceof the proposed clustering analysis is evaluated and compared with that of the Kmeans, SOM andNG methods using different types of data. The clustering results of the proposed method and existingstateoftheart clustering methods are also compared which demonstrates a better accuracy of theproposed clustering method.
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133
142


M.
Ghayekhloo
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Young Researchers and Elite Club, Qazvin
Iran
m.ghayekhlou@gmail.com


M. B.
Menhaj
Dept. of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Electrical Engineering, Amirkabir
Iran
menhaj@aut.ac.ir


R.
Azimi
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Young Researchers and Elite Club, Qazvin
Iran
azimi.rasool@gmail.com


E.
Shekari
Dept. of Decision Science and Knowledge Engineering, University of Economic Sciences, Tehran, Iran
Dept. of Decision Science and Knowledge Engineerin
Iran
e.shekari@gmail.com
clustering
game theory
selforganizing map
vector quantization
[[1] R. Duwairi, M. AbuRahmeh, A novel approach for initializing the spherical Kmeans clustering algorithm, Simulation Modelling Practice and Theory, 54 (2015) 4963. ##[2] H. Mashayekhi, J. Habibi, S. Voulgaris, M. van Steen, GoSCAN: Decentralized scalable data clustering, Computing, 95(9) (2013) 759784. ##[3] S.M.R. Zadegan, M. Mirzaie, F. Sadoughi, Ranked kmedoids: A fast and accurate rankbased partitioning algorithm for clustering large datasets, KnowledgeBased Systems, 39 (2013) 133143. ##[4] J. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Oakland, CA, USA., 1967, pp. 281297. ##[5] H.S. Park, C.H. Jun, A simple and fast algorithm for Kmedoids clustering, Expert systems with applications, 36(2) (2009) 33363341. ##[6] J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact wellseparated clusters, (1973). ##[7] S. Miyamoto, K. Umayahara, Methods in hard and fuzzy clustering, in: Soft computing and humancentered machines, Springer, 2000, pp. 85129. ##[8] T. Johnson, S.K. Singh, Genetic algorithms based enhanced K Strange points clustering algorithm, in: Computing and Network Communications (CoCoNet), 2015 International Conference on, IEEE, 2015, pp. 737741. ##[9] A. Likas, N. Vlassis, J.J. Verbeek, The global kmeans clustering algorithm, Pattern recognition, 36(2) (2003) 451461. ##[10] D. Arthur, S. Vassilvitskii, kmeans++: The advantages of careful seeding, in: Proceedings of the eighteenth annual ACMSIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 2007, pp. 10271035. ##[11] C. Zhang, D. Ouyang, J. Ning, An artificial bee colony approach for clustering, Expert Systems with Applications, 37(7) (2010) 47614767. ##[12] W. Kwedlo, A clustering method combining differential evolution with the Kmeans algorithm, Pattern Recognition Letters, 32(12) (2011) 16131621. ##[13] T. Kohonen, The selforganizing map, Proceedings of the IEEE, 78(9) (1990) 14641480. ##[14] S. Wu, T.W. Chow, Clustering of the selforganizing map using a clustering validity index based on intercluster and intracluster density, Pattern Recognition, 37(2) (2004) 175188. ##[15] Y. Dogan, D. Birant, A. Kut, SOM++: integration of selforganizing map and kmeans++ algorithms, in: International Workshop on Machine Learning and Data Mining in Pattern Recognition, Springer, 2013, pp. 246259. ##[16] A. Neme, S. Hernández, O. Neme, L. Hernández, SelfOrganizing Maps with Noncooperative Strategies (SOMNC), in: WSOM, Springer, 2009, pp. 200208. ##[17] A.P. Engelbrecht, Computational intelligence: an introduction, John Wiley & Sons, 2007. ##[18] L. Pavel, Game theory for control of optical networks, Springer Science & Business Media, 2012. ##[19] J. Shen, S.I. Chang, E.S. Lee, Y. Deng, S.J. Brown, Determination of cluster number in clustering microarray data, Applied Mathematics and Computation, 169(2) (2005) 11721185. ##[20] S. Subramani, S. Balasubramaniam, Post mining of diversified multiple decision trees for actionable knowledge discovery, in: International Conference on Advanced Computing, Networking and Security, Springer, 2011, pp. 179187. ##[21] T. Martinetz, K. Schulten, A" neuralgas" network learns topologies, (1991). ##[22] B. Bahmani, B. Moseley, A. Vattani, R. Kumar, S. Vassilvitskii, Scalable kmeans++, Proceedings of the VLDB Endowment, 5(7) (2012) 622633. ##[23] http://cs.uef.fi/sipu/datasets. ##[24] https://archive.ics.uci.edu/ml/datasets.##]
ThermoElectro Mechanical Impedance based Structural Health Monitoring: Euler Bernoulli Beam Modeling
2
2
In recent years, impedance measurement method by piezoelectric (PZT) wafer activesensor (PWAS) has been widely adopted for nondestructive evaluation (NDE). In this method, theelectrical impedance of a bonded PWAS is used to detect a structural defect. The electromechanicalcoupling of PZT materials constructs the original principle of this method. Accordingly, the electricalimpedance of PWAS can sense any change in the mechanical impedance of the structure. A thermalstress on a structure, which was generated by environmental temperature, could change the electricalimpedance of PWAS. The thermal stress which affects the output impedance of PWAS is alsodeveloped. A temperaturedependent model, the temperature dependency of PWAS, and structurematerial properties are investigated for a PWAS bonded to an Euler Bernoulli clampedclamped beam.The RayleighRitz and spectral element methods are studied and, then, verified by 3D finite elementmethod (FEM).
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143
152


N.
Sepehry
Faculty of Mechanical and Mechatronics Engineering, Shahrood University of Technology, Shahrood, Iran
Faculty of Mechanical and Mechatronics Engineering
Iran
naser.sepehry@gmail.com


F.
BakhtiariNejad
Dept. of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Mechanical Engineering, Amirkabir
Iran
baktiari@aut.ac.ir


M.
Shamshirsaz
New Technologies Research Center, Amirkabir University of Technology, Tehran, Iran
New Technologies Research Center, Amirkabir
Iran
shamshir@aut.ac.ir
Thermal Stress
Euler Bernoulli Beam
Spectral Element Method
Impedancebased Structural Health
Monitoring
3D FEM
[[1] V. Giurgiutiu, C. Rogers, Electromechanical (E/M) impedance method for structural health monitoring and nondestructive evaluation, Structural Health Monitoring—Current Status and Perspective, (1997) 1820. ##[2] G. Park, H.H. Cudney, D.J. Inman, Feasibility of using impedance‐based damage assessment for pipeline structures, Earthquake engineering & structural dynamics, 30(10) (2001) 14631474. ##[3] S. Bhalla, A.S.K. Naidu, C.K. Soh, Influence of structureactuator interactions and temperature on piezoelectric mechatronic signatures for NDE, in: Smart Materials, Structures, and Systems, International Society for Optics and Photonics, 2003, pp. 263270. ##[4] K.Y. Koo, S. Park, J.J. Lee, C.B. Yun, Automated impedancebased structural health monitoring incorporating effective frequency shift for compensating temperature effects, Journal of Intelligent Material Systems and Structures, 20(4) (2009) 367377. ##[5] G. Park, K. Kabeya, H.H. Cudney, D.J. Inman, Impedancebased structural health monitoring for temperature varying applications, JSME International Journal Series A Solid Mechanics and Material Engineering, 42(2) (1999) 249258. ##[6] A. Bastani, H. Amindavar, M. Shamshirsaz, N. Sepehry, Identification of temperature variation and vibration disturbance in impedancebased structural health monitoring using piezoelectric sensor array method, Structural Health Monitoring, 11(3) (2012) 305314. ##[7] N. Sepehry, M. Shamshirsaz, F. Abdollahi, Temperature variation effect compensation in impedancebased structural health monitoring using neural networks, Journal of Intelligent Material Systems and Structures, 22(17) (2011) 19751982. ##[8] N. Sepehry, M. Shamshirsaz, A. Bastani, Experimental and theoretical analysis in impedancebased structural health monitoring with varying temperature, Structural Health Monitoring, 10(6) (2011) 573585. ##[9] V. Giurgiutiu, Structural health monitoring: with piezoelectric wafer active sensors, Academic Press, 2007. ##[10] A.N. Zagrai, V. Giurgiutiu, Electromechanical impedance method for crack detection in thin wall structures, in: 3rd Int. Workshop of Structural Health Monitoring, 2001, pp. 1214. ##[11] S. Bhalla, C.K. Soh, Electromechanical impedance modeling for adhesively bonded piezotransducers, Journal of Intelligent Material Systems and Structures, 15(12) (2004) 955972. ##[12] D.M. Peairs, D.J. Inman, G. Park, Circuit analysis of impedancebased health monitoring of beams using spectral elements, Structural Health Monitoring, 6(1) (2007) 8194. ##[13] S. Bhalla, C.K. Soh, Structural health monitoring by piezoimpedance transducers. I: Modeling, Journal of Aerospace Engineering, 17(4) (2004) 154165. ##[14] W. Yan, W. Chen, C. Lim, J. Cai, Application of EMI technique for crack detection in continuous beams adhesively bonded with multiple piezoelectric patches, Mechanics of Advanced Materials and Structures, 15(1) (2008) 111. ##[15] U. Lee, Spectral element method in structural dynamics, John Wiley & Sons, 2009. ##[16] Y. Kiani, S. Taheri, M. Eslami, Thermal buckling of piezoelectric functionally graded material beams, Journal of Thermal Stresses, 34(8) (2011) 835850.##]
Generalized Aggregate Uncertainty Measure 2 for Uncertainty Evaluation of a DezertSmarandache Theory based Localization Problem
2
2
In this paper, Generalized Aggregated Uncertainty measure 2 (GAU2), as a newuncertainty measure, is considered to evaluate uncertainty in a localization problem in which cameras’images are used. The theory that is applied to a hierarchical structure for a decision making to combinecameras’ images is DezertSmarandache theory. To evaluate decisions, an analysis of uncertainty isexecuted at every level of the decisionmaking system. The second generalization of AggregatedUncertainty measure (GAU2) which is applicable for DSmT results is used as a supervisor. TheGAU2 measure in spite of the GAU1 measure can be applied to the problems with vague borders orcontinuous events. This measure may help to make decisions based on better preference combinationsof sensors or methods of fusion. GAU2 is used to evaluate uncertainty after applying classic DSmTand hybrid DSmT with extra knowledge. Therefore by using the decision making system, results withless uncertainty are generated in spite of high conflict sensory data.
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153
162


A.
MohammadShahri
Dept. of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
Dept. of Electrical Engineering, Iran University
Iran
shahri@iust.ac.ir


M.
Khodabandeh
Dept. of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran
Dept. of Electrical Engineering, Iran University
Iran
khodabandeh@hut.ac.ir
Data Fusion
Camera Image
Uncertainty Measurement
DezertSmarandache Theory
[[1] P. Pong, S. Challa, Empirical analysis of generalized uncertainty measures with DempsterShafer fusion, in: 10th Int. Conf. on Information Fusion, Quebec, Que, 912 July, 2007. ##[2] J. Esteban, Starr A., Willetts R., Hannah P., BryanstonCross P., A review of data fusion models and architectures: towards engineering guidelines, Neural Computing & Application, 14 (2005) 273281. ##[3] G. Shafer, A mathematical theory of evidence, Princeton Univ. Press, Princeton, NJ, 1976. ##[4] R.R. Yager, Hedging in the combination of evidence, Journal of Information Optimization Science, 4 (1983) 7381. ##[5] R.R. Yager, On the Dempster–Shafer framework and new combination rules, Information Science, 41 (1987) 93138. ##[6] P. Smets, R. Kennes, The transferable belief model, Artificial Intelligence, 66(2) 191234. ##[7] D. Dubois, H. Prade, Representation and combination of uncertainty with belief functions and possibility measures, Computational Intelligence, 4 (1988) 244264. ##[8] J.P. Yang, H.Z. Huang, Y. Liu, Y.F. Li, Quantification classification algorithm of multiple sources of evidence, International Journal of Information Technology & Decision Making, 14(5) (2015) 10171034. ##[9] J. Dezert, Foundation for a new theory of plausible and paradoxical reasoning, Information Security Journal, 9 (2002) 1357. ##[10] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 1, American Research Press, 2004. ##[11] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 2, American Research Press, 2006. ##[12] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 3, American Research Press, 2009. ##[13] R.V.L. Hartley, Transmission of information, Bell Systems & Technology Journal, 7(535563) (1928). ##[14] C.E. Shannon, A mathematical theory of communication, Bell Systems & Technology Journal, 27 (1948) 379423 and 623656. ##[15] G.J. Klir, M.J. Wierman, Uncertaintybased information 2nd ed. series, Studies in Fuzziness and Soft Computing 15, Physica–Verlag, Heidelberg, Germany, 1999. ##[16] M. Beynon, D. Cosker, D. Marshall, An expert system for multicriteria decision making using Dempster Shafer theory, Expert Systems with Applications, 20 (2001) 357367. ##[17] Z. Hua, B. Gong, X. Xu, A DS–AHP approach for multiattribute decision making problem with incomplete information, Expert Systems with Applications, 34 (2008) 22212227. ##[18] J. Dezert, J.M. Tacnet, M. BattonHubert, F. Smarandache, Multi criteria decision making based on DSmTAHP, in: Advances and Applications of DSmT for Information Fusion (collected works), American Research Press (ARP), 2015. ##[19] N. Abbas, Y. Chibani, A. Martin, F. Smarandache, The effective use of the DSmT for multiclass classification in: Advances and Applications of DSmT for Information Fusion (collected works), American Research Press (ARP), 2015, pp. 359. ##[20] F. Smarandache, J. Dezert, Advances and applications of DSmT for information fusion, (collected works), vol. 4, American Research Press, 2015. ##[21] R.R. Yager, Entropy and specificity in a mathematical theory of evidence, International Journal of General Systems, 9(4) (1983) 249260. ##[22] G.J. Klir, A. Ramer, Uncertainty in Dempster–Shafer theory: a critical reexamination, International Journal of General Systems, 18(2) (1990) 155166. ##[23] T. George, N.R. Pal, Quantification of conflict in Dempster–Shafer framework: a new approach, International Journal of General Systems, 24(4) (1996) 407423. ##[24] N.R. Pal, J.C. Bezdek, R. Hemasinha, Uncertainty measures for evidential reasoning I: a review, International Journal of Approximate Reasoning, 7(3/4) (1992) 165183. ##[25] N.R. Pal, J.C. Bezdek, R. Hemasinha, Uncertainty measures for evidential reasoning II: a new measure of total uncertainty, International Journal of Approximate Reasoning, 8(1) (1993) 116. ##[26] A. Ramer, J. Hiller, Total uncertainty revisited, International Journal of General Systems, 26(3) (1997) 223237. ##[27] J.A. Herencia, M.T. Lamata, A generalization of entropy using Dempster–Shafer theory, International Journal of General Systems, 29(5) (2000) 719735. ##[28] Y. Maeda, H.T. Nguyen, H. Ichihashi, Maximum entropy algorithms for uncertainty measures, International Journal of Uncertainty, Fuzziness & Knowledge Based Systems, 1(1) (1993) 6993. ##[29] D. Harmanec, G.J. Klir, Measuring total uncertainty in Dempster–Shafer theory, International Journal of General Systems, 22(4) (1994) 405419. ##[30] D. Harmanec, Uncertainty in Dempster–Shafer theory, State University of New York, New York, NY, 1996. ##[31] A.L. Jousselme, C. Liu, D. Grenier, E. Bosse, IEEE Transaction on Systems, Man and CyberneticsPart A: Systems and Humans, 36(5) (2006) 890903. ##[32] G.J. Klir, H.W. Lewis, Remarks on measuring ambiguity in the evidence theory, IEEE Transaction on Systems, Man and CyberneticsPart A: Systems and Humans, 38(4) (2008) 995999. ##[33] M. Vatsa, R. Singh, A. Noore, M.M. Houck, Qualityaugmented fusion of level2 and level3 fingerprint information using DSm theory, International Journal of Approximate Reasoning, 50 (2008) 5161. ##[34] M. Vatsa, R. Singh, A. Noore, Unification of evidencetheoretic fusion algorithms; a case study in level2 and level3 fingerprint features, IEEE Transaction on Systems, Man and CyberneticsPart A: Systems and Humans, 39(1) (2009) 4756. ##[35] G.J. Klir, Uncertainty and information, foundation of generalized information theory, John Wiley & Sons Inc., Hoboken, NJ, 2006. ##[36] M. Khodabandeh, A. MohammadShahri, Two generalizations of aggregated uncertainty measure for evaluation of DezertSmarandache theory, International Journal Information Technology & Decision Making (IJITDM), 11(1) (2012) 119142. ##[37] M. Khodabandeh, A. MohammadShahri, Uncertainty evaluation for a DezertSmarandache theory based localization problem, International Journal of General Systems, 43(6) (2014) 610632. ##[38] M. Khodabandeh, A. MohammadShahri, Uncertainty evaluation for an ultrasonic data fusion based target differentiation problem using Generalized Aggregated Uncertainty measure 2, Measurement, 59 (2015) 139144. ##[39] E. Garcia, Altamirano L., Multiple cameras fusion based on DSmT for tracking objects on ground plane, in: F. Smarandache, Dezert J. (Eds.) Advances and Applications of DSmT for Information Fusion (collected works), American Research Press (ARP), 2009, pp. 691704.##]
NearOptimal Controls of a Fuel Cell Coupled with Reformer using Singular Perturbation methods
2
2
A singularly perturbed model is proposed for a system comprised of a PEM Fuel Cell(PEMFC) with Natural Gas Hydrogen Reformer (NGHR). This eighteenth order system is decomposedinto slow and fast lower order subsystems using singular perturbation techniques that provides tools forseparation and order reduction. Then, three different types of controllers, namely an optimal fullorder,a nearoptimal composite controller based on the slow and the fast subsystems, and a nearoptimalreducedorder controller based on the reducedorder model, are designed. The comparison of closedloopresponses of these three controllers shows that there are minimal degradations in the performanceof the composite and the reduced order controllers.
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163
172


S.
NazemZadeh
Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Dept. of Electrical and Computer Engineering,
Iran
nazemzadeh_sh@yahoo.com


M.T.
HamidiBeheshti
Dept. of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Dept. of Electrical and Computer Engineering,
Iran
mbehesht@modares.ac.ir
singular perturbation technique
twotime scale systems
Schur decomposition method
nearoptimal controller
slow/fast subsystems
[[1] J.T. Pukrushpan, Modeling and control of fuel cell systems and fuel processors, University of Michigan Ann Ar bor, Michigan, USA, 2003. ##[2] J.T. Pukrushpan, A.G. Stefanopoulou, H. Peng, Control of fuel cell breathing, IEEE Control Systems, 24(2) (2004) 3046. ## [3] Pukrushpan, J., Stefanopoulou, A., and Peng, H., Control of fuel cell breathing: Initial Results on the oxygen starvation problem, Ann Arbor, National Science Foundation & Automotive Research Center of University of Michigan. ##[4] J.T. Pukrushpan, A.G. Stefanopoulou, H. Peng, Control of fuel cell power systems: principles, modeling, analysis and feedback design, Springer Science & Business Media, 2004. ##[5] V. Tsourapas, A.G. Stefanopoulou, J. Sun, Modelbased control of an integrated fuel cell and fuel processor with exhaust heat recirculation, IEEE Transactions on control systems technology, 15(2) (2007) 233245. ##[6] K. Kodra, Z. Gajic, Order reduction via balancing and suboptimal control of a fuel cell–reformer system, International Journal of Hydrogen Energy, 39(5) (2014) 22152223. ##[7] J. OReilly, P. Kokotovic, H. Khalil, Singular Perturbation Methods in Control: Analysis and Design, in, Academic Press New York, 1986. ##[8] M. Skataric, Z. Gajic, Slow and fast dynamics of a natural gas hydrogen reformer, International Journal of Hydrogen Energy, 38(35) (2013) 1517315179. ##[9] D.S. Naidu, Singular perturbation methodology in control systems, IET, 1988. ##[10] A. Rao, S. Lamba, S. Rao, Comments on" A note on selecting a loworder system by Davison's model simplification technique, IEEE Transactions on Automatic Control, 24(1) (1979) 141142. ##[11] K.B. Datta, Matrix and linear algebra, PrenticeHall of India New Delhi, India, 1991. ##[12] B. Noble, J.W. Daniel, Applied linear algebra, PrenticeHall New Jersey, 1988. ##[13] A.J. Fossard, M. Berthelot, J. Magni, On coherencybased decomposition algorithms, Automatica, 19(3) (1983) 247253.##]
A Hybrid Modeling for Continuous Casting Scheduling Problem
2
2
This paper deals with a multiagentbased interval type2 fuzzy (IT2F) expert systemfor scheduling steel continuous casting. Continuous caster scheduling is a complex and extensiveprocess that needs expert staff. In this study, a distributed multiagentbased structure is proposed as asolution. The agents used herein can cooperate with each other via various communication protocols.To facilitate such communication, an appropriate negotiation protocol (i.e., contract net protocol)is proposed. The due dates specified by expert staff are represented by IT2F membership functions(MFs). As a part of the objective functions, a simple procedure is proposed to calculate the totalearliness and tardiness penalty when the due date’s MFs are IT2F. The proposed hybrid multiagentbasedsystem combines the multiagent systems with type2 fuzzy concepts which conforms to therealworld continuous casting problem.
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173
180


M. H.
Fazel Zarandi
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Industrial Engineering, Amirkabir
Iran
zarandi@aut.ac.ir


F.
Kashani Azad
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Industrial Engineering, Amirkabir
Iran
f_kashani90@aut.ac.ir


A. H.
Karimi Kashani
Dept. of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Industrial Engineering, Amirkabir
Iran
karimikashani54@yahoo.com
Steel production
Continuous caster scheduling
Agentbased system
Negotiation
Fuzzy system
[[1] D. Ouelhadj, A multiagent system for the integrated dynamic scheduling of steel production, University of Nottingham, 2003. ##[2] P.I. Cowling, D. Ouelhadj, S. Petrovic, Dynamic scheduling of steel casting and milling using multiagents, Production Planning & Control, 15(2) (2004) 178188. ##[3] Y. Li, J.Q. Zheng, S.L. Yang, Multiagentbased fuzzy scheduling for shop floor, The International Journal of Advanced Manufacturing Technology, 49(5) (2010) 689695. ##[4] M.F. Zarandi, P. Ahmadpour, Fuzzy agentbased expert system for steel making process, Expert systems with applications, 36(5) (2009) 95399547. ##[5] O. Castillo, P. Melin, J. Kacprzyk, W. Pedrycz, Type2 fuzzy logic: theory and applications, in: Granular Computing, 2007. GRC 2007. IEEE International Conference on, IEEE, 2007, pp. 145145. ##[6] N.N. Karnik, J.M. Mendel, Operations on type2 fuzzy sets, Fuzzy sets and systems, 122(2) (2001) 327348. ##[7] J.M. Mendel, R.B. John, Type2 fuzzy sets made simple, IEEE Transactions on fuzzy systems, 10(2) (2002) 117127. ##[8] M.F. Zarandi, R. Gamasaee, Type2 fuzzy hybrid expert system for prediction of tardiness in scheduling of steel continuous casting process, Soft Computing, 16(8) (2012) 12871302. ##[9] Q. Liang, J.M. Mendel, Interval type2 fuzzy logic systems: theory and design, IEEE Transactions on Fuzzy systems, 8(5) (2000) 535550. ##[10] J. Dorn, W. Slany, A flow shop with compatibility constraints in a steelmaking plant, na, 1994. ##[11] J. Dorn, Iterative improvement methods for knowledgebased scheduling, AI communications, 8(1) (1995) 2034. ##[12] F. Liu, J.M. Mendel, An interval approach to fuzzistics for interval type2 fuzzy sets, in: Fuzzy Systems Conference, 2007. FUZZIEEE 2007. IEEE International, IEEE, 2007, pp. 16. ##[13] B. Lally, L. Biegler, H. Henein, A model for sequencing a continuous casting operation to minimize costs, Mathematical Modelling of Materials Processing Operations, (1987) 11571172. ##[14] L. Tang, J. Liu, A. Rong, Z. Yang, A mathematical programming model for scheduling steelmakingcontinuous casting production, European Journal of Operational Research, 120(2) (2000) 423435. ##[15] S.Y. Chang, M.R. Chang, Y. Hong, A lot grouping algorithm for a continuous slab caster in an integrated steel mill, Production planning & control, 11(4) (2000) 363368.##]
Developing a Model for Measuring Severity of Effects Caused by Interconnected Units in Electronic Supply Chains
2
2
For many electronic supply chain networks in the world that can comprise hundreds ofcompanies with several tiers of suppliers and intermediate customers, there are numerous presentingrisks to consider. In the electronic supply chain, the situation are even worse, for the characteristics ofthis supply chain: excessive lean management, global sourcing and the rather more uncertain marketdemand. Electronic companies are forced to manage their supply chains effectively to increase efficiencyand reactivity. This paper proposes a mathematical model for estimating the severity of interactionsbetween supply chain’s units and how their affect on the entire supply chain. Based on the model,scholars can model supply chains easily with considering interconnected units. Basic characteristics ofsupply chains are considered in the model. The units, which are used to simulate the members of supplychains, produce appropriate products by intelligent choices. The relationships on units are connected bytheir activities ; then, the proposed model is applied to an experimental example. The model yields itsnumerical parameters and responses by means of Lingo software.
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181
186


A.
Kazemi
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering,
Iran
abkaazemi@gmail.com


L.
Ahmadpour
Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Faculty of Industrial and Mechanical Engineering,
Iran
leila8061@gmail.com
Supply chain
Interconnected units
Propagated effect
[ [1] G. Applequist, J. Pekny, G. Reklaitis, Risk and uncertainty in managing chemical manufacturing supply chains, Computers & Chemical Engineering, 24(910) (2000) 22112222. ##[2] T. Assavapokee, W. Wongthatsanekorn, Reverse production system infrastructure design for electronic products in the state of Texas, Computers & Industrial Engineering, 62(1) (2012) 129140. ##[3] W.H. Baker, L. Wallace, Is information security under control?: Investigating quality in information security management, IEEE Security & Privacy, 5(1) (2007). ##[4] S. Bose, J. Pekny, A model predictive framework for planning and scheduling problems: a case study of consumer goods supply chain, Computers & Chemical Engineering, 24(27) (2000) 329335. ##[5] M.A. Cohen, H.L. Lee, Resource deployment analysis of global manufacturing and distribution networks, Journal of manufacturing and operations management, 2(2) (1989) 81104. ##[6] C.W. Craighead, J. Blackhurst, M.J. Rungtusanatham, R.B. Handfield, The severity of supply chain disruptions: design characteristics and mitigation capabilities, Decision Sciences, 38(1) (2007) 131156. ##[7] F.D. Mele, G. Guillén, A. Espuna, L. Puigjaner, An agentbased approach for supply chain retrofitting under uncertainty, Computers & chemical engineering, 31(56) (2007) 722735. ##[8] P. Georgiadis, M. Besiou, Sustainability in electrical and electronic equipment closedloop supply chains: a system dynamics approach, Journal of Cleaner Production, 16(15) (2008) 16651678. ##[9] A. Gupta, C.D. Maranas, A twostage modeling and solution framework for multisite midterm planning under demand uncertainty, Industrial & Engineering Chemistry Research, 39(10) (2000) 37993813. ##[10] A. Gupta, C.D. Maranas, Managing demand uncertainty in supply chain planning, Computers & chemical engineering, 27(89) (2003) 12191227. ##[11] J.K. Deane, C.T. Ragsdale, T.R. Rakes, L.P. Rees, Managing supply chain risk and disruption from IT security incidents, Operations Management Research, 2(14) (2009) 4. ##[12] J. Li, F.T. Chan, An agentbased model of supply chains with dynamic structures, Applied Mathematical Modelling, 37(7) (2013) 54035413. ##[13] Y. Li, Z. Lin, L. Xu, A. Swain, “Do the electronic books reinforce the dynamics of book supply chain market?”–A theoretical analysis, European Journal of Operational Research, 245(2) (2015) 591601. ##[14] P.K. Marhavilas, D. Koulouriotis, V. Gemeni, Risk analysis and assessment methodologies in the work sites: on a review, classification and comparative study of the scientific literature of the period 2000–2009, Journal of Loss Prevention in the Process Industries, 24(5) (2011) 477523. ##[15] A. Mele, Asymmetric stock market volatility and the cyclical behavior of expected returns, Journal of financial economics, 86(2) (2007) 446478. ##[16] S. Mohebbi, X. Li, Designing intelligent agents to support longterm partnership in two echelon eSupply Networks, Expert Systems with Applications, 39(18) (2012) 1350113508. ##[17] A. Nagurney, J. Cruz, J. Dong, D. Zhang, Supply chain networks, electronic commerce, and supply side and demand side risk, European journal of operational research, 164(1) (2005) 120142. ##[18] E. PereaLopez, B.E. Ydstie, I.E. Grossmann, A model predictive control strategy for supply chain optimization, Computers & Chemical Engineering, 27(89) (2003) 12011218. ##[19] M. Sakawa, I. Nishizaki, Y. Uemura, Fuzzy programming and profit and cost allocation for a production and transportation problem, European Journal of Operational Research, 131(1) (2001) 115. ##[20] D. SimchiLevi, E. SimchiLevi, P. Kaminsky, Designing and managing the supply chain: Concepts, strategies, and cases, McGrawHill New York, 1999. ##[21] G. Stoneburner, A.Y. Goguen, A. Feringa, Sp 80030. risk management guide for information technology systems, (2002). ##[22] C.H. Timpe, J. Kallrath, Optimal planning in large multisite production networks, European Journal of Operational Research, 126(2) (2000) 422435. ##[23] W. Wang, Y. Zhang, Y. Li, X. Zhao, M. Cheng, Closedloop supply chains under rewardpenalty mechanism: Retailer collection and asymmetric information, Journal of cleaner production, 142 (2017) 39383955.##]
Partial Observation in Distributed Supervisory Control of DiscreteEvent Systems
2
2
Distributed supervisory control is a method to synthesize local controllers in discreteeventsystems with a systematic observation of the plant. Some works were reported on extending this methodby which local controllers are constructed so that observation properties are preserved from monolithic todistributed supervisory control, in an updown approach. In this paper, we find circumstances in whichobservation properties are preserved from monolithic to distributed supervisory control. Local observationproperties, i.e. local normality and local relative observability are employed for investigating observationproperties of each local controller, which are constructed by any localization algorithm that preserves controlequivalency to the monolithic supervisor with respect to the plant. These properties enable us to investigatethe observation properties from monolithic to distributed supervisory control. Moreover, observationequivalence property is defined according to the control equivalence in a distributed supervisory controlwith partial observation. It is proved that with preserving observation equivalence of the local controllers tothe monolithic supervisor, the control equivalence is satisfied, if and only if the intersection of local eventsets is a subset of or equal to the global observable event set.
1

187
198


V.
Saeidi
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Abbaspour
Iran
vahidsaidi@gmail.com


A.
Afzalian
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Abbaspour
Iran
afzalian@sbu.ac.ir


D.
Gharavian
Dept. of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Abbaspour
Iran
d_gharavian@sbu.ac.ir
Distributed Supervisory Control
Local Normality
Local Relative Observability
Observation Equivalent
[[1] P.J. Ramadge, W.M. Wonham, Supervisory control of a class of discrete event processes, SIAM journal on control and optimization, 25(1) (1987) 206230. ##[2] F. Lin, W.M. Wonham, Decentralized control and coordination of discreteevent systems with partial observation, IEEE Transactions on automatic control, 35(12) (1990) 13301337. ##[3] J. Komenda, J.H. van Schuppen, Modular control of discreteevent systems with coalgebra, IEEE Transactions on Automatic Control, 53(2) (2008) 447460. ##[4] H. Zhong, W.M. Wonham, On the consistency of hierarchical supervision in discreteevent systems, IEEE Transactions on automatic Control, 35(10) (1990) 11251134. ##[5] K.C. Wong, W.M. Wonham, Hierarchical control of discreteevent systems, Discrete Event Dynamic Systems, 6(3) (1996) 241273. ##[6] K.C. Wong, W.M. Wonham, Modular control and coordination of discreteevent systems, Discrete Event Dynamic Systems, 8(3) (1998) 247297. ##[7] K. Schmidt, T. Moor, S. Perk, Nonblocking hierarchical control of decentralized discrete event systems, IEEE Transactions on Automatic Control, 53(10) (2008) 22522265. ##[8] K. Schmidt, C. Breindl, Maximally permissive hierarchical control of decentralized discrete event systems, IEEE Transactions on Automatic Control, 56(4) (2011) 723737. ##[9] L. Feng, W.M. Wonham, Supervisory control architecture for discreteevent systems, IEEE Transactions on Automatic Control, 53(6) (2008) 14491461. ##[10] T.S. Yoo, S. Lafortune, A general architecture for decentralized supervisory control of discreteevent systems, Discrete Event Dynamic Systems, 12(3) (2002) 335377. ##[11] K. Rudie, W.M. Wonham, Think globally, act locally: Decentralized supervisory control, IEEE transactions on automatic control, 37(11) (1992) 16921708. ##[12] K. Cai, R. Zhang, W.M. Wonham, On relative observability of discreteevent systems, in: Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on, IEEE, 2013, pp. 72857290. ## [13] J. Komenda, T. Masopust, J.H. van Schuppen, Coordination control of discreteevent systems revisited, Discrete Event Dynamic Systems, 25(12) (2015) 6594. ##[14] J. Komenda, T. Masopust, J.H. van Schuppen, On conditional decomposability, Systems & Control Letters, 61(12) (2012) 12601268. ##[15] K. Cai, W.M. Wonham, Supervisor localization: a topdown approach to distributed control of discreteevent systems, IEEE Transactions on Automatic Control, 55(3) (2010) 605618. ##[16] F. Lin, W.M. Wonham, On observability of discreteevent systems, Information sciences, 44(3) (1988) 173198. ##[17] K. Cai, R. Zhang, W.M. Wonham, Relative observability of discreteevent systems and its supremal sublanguages, IEEE Transactions on Automatic Control, 60(3) (2015) 659670. ##[18] E. José, N. Patrícia, L. Stéphane, Verification of Nonconflict of Supervisors Using Abstractions, (2009). ##[19] R. Zhang, K. Cai, W.M. Wonham, Supervisor localization of discreteevent systems under partial observation, Automatica, 81 (2017) 142147. ##[20] V. Saeidi, A.A. Afzalian, D. Gharavian, Distributed supervisory control with partial observation, in: Control, Instrumentation, and Automation (ICCIA), 2016 4th International Conference on, IEEE, 2016, pp. 136141. ##[21] S. Mohajerani, R. Malik, M. Fabian, An algorithm for weak synthesis observation equivalence for compositional supervisor synthesis, IFAC Proceedings Volumes, 45(29) (2012) 239244. ##[22] M. Noorbakshsh, A. Afzalian, Design and PLC based implementation of supervisory control for underload tapchanging transformers, in: Control, Automation and Systems, 2007. ICCAS'07. International Conference on, IEEE, 2007, pp. 901906. ##[23] A. Afzalian, A. Saadatpoor, W. Wonham, Systematic supervisory control solutions for underload tapchanging transformers, Control Engineering Practice, 16(9) (2008) 10351054. ##[24] R. Zhang, K. Cai, Y. Gan, W.M. Wonham, Distributed supervisory control of discreteevent systems with communication delay, Discrete Event Dynamic Systems, 26(2) (2016) 263293. ##[25] W. Wonham, Control design software: TCT, Developed by Systems Control Group, Univ. Toronto. Toronto, Canada, (2014).##]
Saturated Neural Adaptive Robust Output Feedback Control of Robot Manipulators:An Experimental Comparative Study
2
2
In this study, an observerbased tracking controller is proposed and evaluatedexperimentally to solve the trajectory tracking problem of robotic manipulators with the torque saturationin the presence of model uncertainties and external disturbances. In comparison with the stateoftheartobserverbased controllers in the literature, this paper introduces a saturated observerbased controllerbased on a radial basis function neural network. This technique helps the controller produce feasiblecontrol signals for the robot actuators. As a result, it efficiently diminishes the actuators saturation riskand consequently, a better transient performance is obtained. The stability analyses of the dynamicsof the tracking errors and state estimation errors are given with the help of a Lyapunovbased stabilityanalysis method. The theoretical analyses will systematically prove that the errors are semigloballyuniformly ultimately bounded and they converge to a small set around the origin whose size is adjustableby a suitable tuning of parameters. At last, some real experiments are performed on a laboratory roboticarm to illustrate the efficiency of the proposed control system for real industrial applications.
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199
208


M.
Pourrahim
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad
Iran
pde.mohammad@gmail.com


K.
Shojaei
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad
Iran
khoshnam.shojaee@gmail.com


A.
Chatraei
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad
Iran
abbas.chatraei@gmail.com


O.
Shahnazari
Dept. of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Dept. of Electrical Engineering, Najafabad
Iran
dep_omid@yahoo.com
Actuator saturation
Adaptive robust control
Observerbased control
RBF neural networks
Robot manipulators
[[1] H. Berghuis, H. Nijmeijer, A passivity approach to controllerobserver design for robots, IEEE Transactions on robotics and automation, 9(6) (1993) 740754. ##[2] M.A. Arteaga, R. Kelly, Robot control without velocity measurements: New theory and experimental results, IEEE Transactions on Robotics and Automation, 20(2) (2004) 297308. ##[3] D.J. LópezAraujo, A. ZavalaRío, V. Santibáñez, F. Reyes, Outputfeedback adaptive control for the global regulation of robot manipulators with bounded inputs, International Journal of Control, Automation and Systems, 11(1) (2013) 105115. ##[4] M. Mendoza, A. ZavalaRío, V. Santibáñez, F. Reyes, Outputfeedback proportional–integral–derivativetype control with simple tuning for the global regulation of robot manipulators with input constraints, IET Control Theory & Applications, 9(14) (2015) 20972106. ##[5] W.E. Dixon, Adaptive regulation of amplitude limited robot manipulators with uncertain kinematics and dynamics, IEEE Transactions on Automatic Control, 52(3) (2007) 488493. ##[6] C. Huang, X. Peng, C. Jia, J. Huang, Guaranteed robustness/performance adaptive control with limited torque for robot manipulators, Mechatronics, 18(10) (2008) 641652. ##[7] W.E. Dixon, M.S. de Queiroz, F. Zhang, D.M. Dawson, Tracking control of robot manipulators with bounded torque inputs, Robotica, 17(2) (1999) 121129. ##[8] E. AguiñagaRuiz, A. ZavalaRío, V. Santibanez, F. Reyes, Global trajectory tracking through static feedback for robot manipulators with bounded inputs, IEEE Transactions on Control Systems Technology, 17(4) (2009) 934944. ##[9] A. Laib, Adaptive output regulation of robot manipulators under actuator constraints, IEEE Transactions on Robotics and Automation, 16(1) (2000) 2935. ## [10] Y. Su, P.C. Muller, C. Zheng, Global asymptotic saturated PID control for robot manipulators, IEEE Transactions on Control Systems Technology, 18(6) (2010) 12801288. ## [11] V. Santibañez, K. Camarillo, J. MorenoValenzuela, R. Campa, A practical PID regulator with bounded torques for robot manipulators, International Journal of Control, Automation and Systems, 8(3) (2010) 544555. ##[12] A. Loria, R. Kelly, R. Ortega, V. Santibanez, On global output feedback regulation of EulerLagrange systems with bounded inputs, IEEE Transactions on Automatic Control, 42(8) (1997) 11381143. ##[13] J. MorenoValenzuela, V. Santibáñez, R. Campa, On output feedback tracking control of robot manipulators with bounded torque input, International Journal of Control, Automation, and Systems, 6(1) (2008) 7685. ## [14] F.L. Lewis, D.M. Dawson, C.T. Abdallah, Robot manipulator control: theory and practice, CRC Press, 2003. ##[15] M.W. Spong, S. Hutchinson, M. Vidyasagar, Robot modeling and control, Wiley New York, 2006. ## [16] P.A. Ioannou, J. Sun, Robust adaptive control, PTR PrenticeHall Upper Saddle River, NJ, 1996. ##[17] B. Yao, Adaptive robust control of nonlinear systems with application to control of mechanical systems, University of California, Berkeley, 1996. ##[18] L. Xu, B. Yao, Output feedback adaptive robust precision motion control of linear motors, Automatica, 37(7) (2001) 10291039. ##[19] K. Shojaei, A. Chatraei, A Saturating Extension of an Output Feedback Controller for Internally Damped Euler‐Lagrange Systems, Asian Journal of Control, 17(6) (2015) 21752187. ##[20] M. Pourrahim, K. Shojaei, A. Chatraei, O.S. Nazari, Experimental evaluation of a saturated output feedback controller using RBF neural networks for SCARA robot IBM 7547, in: Electrical Engineering (ICEE), 2016 24th Iranian Conference on, IEEE, 2016, pp. 13471352.##]
Adaptive Control Strategy for a Bilateral Tele Surgery System Interacting with Active Soft Tissues
2
2
In this paper, the problem of control and stabilization of a bilateral telesurgery roboticsystem in interaction with an active soft tissue is considered. To the best of the authors’ knowledge, theprevious works did not consider a realistic model for a moving soft tissue like heart tissue in beating heartsurgery. Here, a new model is proposed to indicate significant characteristics of a moving soft tissue,rolling as the teleoperation system environment. The model is formed by a parallel combination of aviscoelastic passive part and an active part. Furthermore, the delays in communication and parameteruncertainties of the master and slave robot dynamics are considered. Using an adaptive control strategy,the ultimate boundedness of the system trajectories while interacting with the active environment iscertified, and this ultimate bound is calculated. Moreover, to evaluate the theoretical results, simulationresults are presented.
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209
216


M.
Sharifi
1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
1 Department of Electrical Engineering, Amirkabir
Iran
sharifi.m@ut.ac.ir


H. A.
Talebi
1 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
1 Department of Electrical Engineering, Amirkabir
Iran
alit@aut.ac.ir
active soft tissue
viscoelastic model
bilateral tele robotic surgery
communication time delay
Adaptive control
[[1] N. Chopra, M.W. Spong, R. Lozano, Synchronization of bilateral teleoperators with time delay, Automatica, 44(8) (2008) 21422148. ##[2] N. Chopra, M.W. Spong, Passivitybased control of multiagent systems, in: Advances in robot control, Springer, 2006, pp. 107134. ## [3] N. Chopra, M.W. Spong, R. Ortega, N.E. Barabanov, On tracking performance in bilateral teleoperation, IEEE Transactions on Robotics, 22(4) (2006) 861866. ##[4] X. Liu, M. Tavakoli, Inverse dynamicsbased adaptive control of nonlinear bilateral teleoperation systems, in: Robotics and Automation (ICRA), 2011 IEEE International Conference on, IEEE, 2011, pp. 13231328. ##[5] X. Liu, R. Tao, M. Tavakoli, Adaptive control of uncertain nonlinear teleoperation systems, Mechatronics, 24(1) (2014) 6678. ##[6] N. Chopra, M.W. Spong, R. Lozano, Synchronization of bilateral teleoperators with time delay, Automatica, 44(8) (2008) 21422148. ##[7] E. Nuño, R. Ortega, L. Basañez, An adaptive controller for nonlinear teleoperators, Automatica, 46(1) (2010) 155159. ##[8] I.G. Polushin, P.X. Liu, C.H. Lung, G.D. On, Positionerror based schemes for bilateral teleoperation with time delay: theory and experiments, Journal of dynamic systems, measurement, and control, 132(3) (2010) 031008. ##[9] C.C. Hua, X.P. Liu, Delaydependent stability criteria of teleoperation systems with asymmetric timevarying delays, IEEE Transactions on Robotics, 26(5) (2010) 925932. ##[10] F. Hashemzadeh, M. Tavakoli, Position and force tracking in nonlinear teleoperation systems under varying delays, Robotica, 33(4) (2015) 10031016. ##[11] P. Moreira, N. Zemiti, C. Liu, P. Poignet, Viscoelastic model based force control for soft tissue interaction and its application in physiological motion compensation, Computer methods and programs in biomedicine, 116(2) (2014) 5267. ##[12] L. Loeffler, K. Sagawa, A onedimensional viscoelastic model of cat heart muscle studied by small length perturbations during isometric contraction, Circulation research, 36(4) (1975) 498512. ##[13] Y.c. Fung, Biomechanics: mechanical properties of living tissues, Springer Science & Business Media, 2013. ##[14] L. Barbé, B. Bayle, M. de Mathelin, A. Gangi, In vivo model estimation and haptic characterization of needle insertions, The International Journal of Robotics Research, 26(1112) (2007) 12831301. ##[15] W. Bachta, P. Renaud, E. Laroche, A. Forgione, J. Gangloff, Cardiolock: An active cardiac stabilizer. First in vivo experiments using a new robotized device, Computer Aided Surgery, 13(5) (2008) 243254. ##[16] F.L. Lewis, C.T. Abdallah, D.M. Dawson, Control of robot manipulators, Macmillan New York, 1993. ##[17] P. Moreira, C. Liu, N. Zemiti, P. Poignet, Soft tissue force control using active observers and viscoelastic interaction model, in: Robotics and Automation (ICRA), 2012 IEEE International Conference on, IEEE, 2012, pp. 46604666. ##[18] H.K. Khalil, Nonlinear systems. 2002, ISBN, 130673897 (2002) 9780130673893. ## [19] P. Jordan, S. Socrate, T. Zickler, R. Howe, Constitutive modeling of porcine liver in indentation using 3D ultrasound imaging, Journal of the mechanical behavior of biomedical materials, 2(2) (2009) 192201. ##[20] Y. Kobayashi, A. Onishi, H. Watanabe, T. Hoshi, K. Kawamura, M.G. Fujie, In vitro validation of viscoelastic and nonlinear physical model of liver for needle insertion simulation, in: Biomedical Robotics and Biomechatronics, 2008. BioRob 2008. 2nd IEEE RAS & EMBS International Conference on, IEEE, 2008, pp. 469476. ##[21] S. Bhasin, K. Dupree, P.M. Patre, W.E. Dixon, Neural network control of a robot interacting with an uncertain viscoelastic environment, IEEE Transactions on Control Systems Technology, 19(4) (2011) 947955. ##[22] M. Sharifi, H.A. Talebi, Adaptive control of a telerobotic surgery system interacting with nonpassive soft tissues, in: Control, Instrumentation, and Automation (ICCIA), 2016 4th International Conference on, IEEE, 2016, pp. 214219.##]
3RPS Parallel Manipulator Dynamical Modelling and Control Based on SMC and FL Methods
2
2
In this paper, a dynamical modelbased SMC (Sliding Mode Control) is proposed fortrajectory tracking of a 3RPS (Revolute, Prismatic, Spherical) parallel manipulator. With ignoring smallinertial effects of all legs and joints compared with those of the endeffector of 3RPS, the dynamical model ofthe manipulator is developed based on Lagrange method. By removing the unknown Lagrange multipliers, thedistribution matrix of control input vector disappears from the dynamical equations. Therefore, the calculationof the aforementioned matrix is not required for modeling the manipulator. It in trun results in decreasedmathematical manipulation and low computational burden. As a robust nonlinear control technique, a SMCsystem is designed for the tracking of the 3RPS manipulator. According to Lyapunov’s direct method, theasymptotic stability and the convergence of 3RPS manipulator to the desired reference trajectories areproved. Based on computer simulations, the robust performance of the proposed SMC system is evaluatedwith respect to FL (feedback linearization) method. The proposed model and control algorithms can beextended to different kinds of holonomic and nonholonomic constrained parallel manipulators.
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217
226


M.
Shahidi
Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Faculty of Mechanical Engineering, University
Iran
m.shahidi@tabrizu.ac.ir


J.
Keighobadi
Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran
Faculty of Mechanical Engineering, University
Iran
keighobadi@tabrizu.ac.ir


A. R.
Khoogar
Department of Mechanical Engineering, MalekeAshtar University of Technology, Tehran, Iran
Department of Mechanical Engineering, MalekeAshta
Iran
khoogar@yahoo.com
Parallel manipulator
Dynamic modeling
Trajectory tracking
Feedback linearization
sliding mode control
[[1] P. Nanua, K.J. Waldron, V. Murthy, Direct kinematic solution of a Stewart platform, IEEE Transactions on Robotics and Automation, 6(4) (1990) 438444. ##[2] P. Ji, H. Wu, A closedform forward kinematics solution for the 66/sup p/Stewart platform, IEEE Transactions on robotics and automation, 17(4) (2001) 522526. ##[3] J. Schadlbauer, D. Walter, M. Husty, The 3RPS parallel manipulator from an algebraic viewpoint, Mechanism and Machine Theory, 75 (2014) 161176. ##[4] J.P. Merlet, Parallel robots, Springer Science & Business Media, 2006. ##[5] J.P. Merlet, Direct kinematics of parallel manipulators, IEEE transactions on robotics and automation, 9(6) (1993) 842846. ##[6] C.f. Yang, S.t. Zheng, J. Jin, S.b. Zhu, J.w. Han, Forward kinematics analysis of parallel manipulator using modified global NewtonRaphson method, Journal of Central South University of Technology, 17(6) (2010) 12641270. ##[7] W.H. Ding, H. Deng, Q.M. Li, Y.M. Xia, Controlorientated dynamic modeling of forging manipulators with multiclosed kinematic chains, Robotics and ComputerIntegrated Manufacturing, 30(5) (2014) 421431. ##[8] S.H. Lee, J.B. Song, W.C. Choi, D. Hong, Position control of a Stewart platform using inverse dynamics control with approximate dynamics, Mechatronics, 13(6) (2003) 605619. ##[9] M.J. Liu, C.X. Li, C.N. Li, Dynamics analysis of the GoughStewart platform manipulator, IEEE Transactions on Robotics and Automation, 16(1) (2000) 9498. ##[10] W. Khalil, S. Guegan, Inverse and direct dynamic modeling of GoughStewart robots, IEEE Transactions on Robotics, 20(4) (2004) 754761. ##[11] H. Pendar, M. Vakil, H. Zohoor, Efficient dynamic equations of 3RPS parallel mechanism through Lagrange method, in: Robotics, Automation and Mechatronics, 2004 IEEE Conference on, IEEE, 2004, pp. 11521157. ##[12] E. Özgür, N. Andreff, P. Martinet, Linear dynamic modeling of parallel kinematic manipulators from observable kinematic elements, Mechanism and Machine Theory, 69 (2013) 7389. ##[13] M. DiazRodriguez, A. Valera, V. Mata, M. Valles, Modelbased control of a 3DOF parallel robot based on identified relevant parameters, IEEE/ASME Transactions on Mechatronics, 18(6) (2013) 17371744. ##[14] M. Zeinali, L. Notash, Adaptive sliding mode control with uncertainty estimator for robot manipulators, Mechanism and Machine Theory, 45(1) (2010) 8090. ##[15] J. Cazalilla, M. Vallés, V. Mata, M. DíazRodríguez, A. Valera, Adaptive control of a 3DOF parallel manipulator considering payload handling and relevant parameter models, Robotics and ComputerIntegrated Manufacturing, 30(5) (2014) 468477. ##[16] M.R. Sirouspour, S.E. Salcudean, Nonlinear control of hydraulic robots, IEEE Transactions on Robotics and Automation, 17(2) (2001) 173182. ##[17] I. Davliakos, E. Papadopoulos, Modelbased control of a 6dof electrohydraulic Stewart–Gough platform, Mechanism and machine theory, 43(11) (2008) 13851400. ##[18] M.A. Khosravi, H.D. Taghirad, Robust PID control of fullyconstrained cable driven parallel robots, Mechatronics, 24(2) (2014) 8797. ##[19] J.M. Yang, J.H. Kim, Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots, IEEE Transactions on robotics and automation, 15(3) (1999) 578587. ##[20] M.A. Hussain, P.Y. Ho, Adaptive sliding mode control with neural network based hybrid models, Journal of Process Control, 14(2) (2004) 157176. ##[21] P. Doostdar, J. Keighobadi, Design and implementation of SMO for a nonlinear MIMO AHRS, Mechanical Systems and Signal Processing, 32 (2012) 94115. ##[22] K.M. Lee, D.K. Shah, Kinematic analysis of a threedegreesoffreedom inparallel actuated manipulator, IEEE Journal on Robotics and Automation, 4(3) (1988) 354360. ##[23] J.J. Craig, Introduction to robotics: mechanics and control, Pearson Prentice Hall Upper Saddle River, 2005. ##[24] X. Yang, H. Wu, Y. Li, B. Chen, A dual quaternion solution to the forward kinematics of a class of sixDOF parallel robots with full or reductant actuation, Mechanism and Machine Theory, 107 (2017) 2736. ##[25] K.H. Harib, Dynamic modeling, identification and control of Stewart platformbased machine tools, The Ohio State University, 1997. ##[26] L.W. Tsai, Robot analysis: the mechanics of serial and parallel manipulators, John Wiley & Sons, 1999. ##[27] F.L. Lewis, C.T. Abdallah, D.M. Dawson, Control of robot manipulators, Macmillan New York, 1993.##]
A Comparison Between Fourier Transform Adomian Decomposition Method and Homotopy Perturbation ethod for Linear and NonLinear NewellWhiteheadSegel Equations
2
2
In this paper, a comparison among the hybrid of Fourier Transform and AdomianDecomposition Method (FTADM) and Homotopy Perturbation Method (HPM) is investigated.The linear and nonlinear NewellWhiteheadSegel (NWS) equations are solved and the results arecompared with the exact solution. The comparison reveals that for the same number of componentsof recursive sequences, the error of FTADM is much smaller than that of HPM. For the nonlinearNWS equation, the accuracy of FTADM is more pronounced than HPM. Moreover, it is shown thatas time increases, the results of FTADM, for the linear NWS equation, converges to zero. And for thenonlinear NWS equation, the results of FTADM converges to 1 with only six recursive components.This is in agreement with the basic physical concept of NWS diffusion equation which is in turn inagreement with the exact solution.
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227
238


S. S.
Nourazar
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Mechanical Engineering, Amirkabir
Iran
icp@aut.ac.ir


H.
Parsa
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Mechanical Engineering, Amirkabir
Iran
hasan_parsa@aut.ac.ir


A.
Sanjari
Department of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Mechanical Engineering, Amirkabir
Iran
a_70_s@yahoo.com
Fourier Transform and Adomian
Decomposition Method
Homotopy Perturbation Method
Newell–WhiteheadSegel Equation
Nonlinear Partial Differential
Equation
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A Survey of Dynamic Replication Strategies for Improving Response Time in Data Grid Environment
2
2
Largescale data management is a critical problem in a distributed system such as cloud,P2P system, World Wide Web (WWW), and Data Grid. One of the effective solutions is data replicationtechnique, which efficiently reduces the cost of communication and improves the data reliability andresponse time. Various replication methods can be proposed depending on when, where, and howreplicas are generated and removed. In this paper, different replication algorithms are investigated todetermine which attributes are assumed in a given algorithm and which are declined. We provide a tabularrepresentation of important factors to facilitate the future comparison of data replication algorithms. Thispaper also presents some interesting discussions about future works in data replication by proposingsome open research challenges.
1

239
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N.
Mansouri
Computer Science Department, Shahid Bahonar University of Kerman, Kerman, Iran
Computer Science Department, Shahid Bahonar
Iran
najme.mansouri@gmail.com


M. M.
Javidi
Computer Science Department, Shahid Bahonar University of Kerman, Kerman, Iran
Computer Science Department, Shahid Bahonar
Iran
javidi@mail.uk.ac.ir
Data Grid
Dynamic Replication
Data Availability
Simulation
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