2018
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Decentralized Model Reference Adaptive Control of Large Scale Interconnected Systems with Both State and Input Delays
2
2
In this paper, the problem of decentralized Model Reference Adaptive Control (MRAC) for interconnected large scale systems associated with time varying delays in state and input is investigated. The upper bounds of the interconnection terms are considered to be unknown. Time varying delays in the nonlinear interconnection terms are bounded and nonnegative continuous functions and their derivatives are not necessarily less than one. Moreover, a simple and practical method based on periodic characteristics of the reference model is established to predict the future states and input delay compensation. It is shown that the solution of uncertain largescale timedelay interconnected system converges uniformly exponentially to inside of a desired small ball. Simulation results of a chemical reactor system and a numerical example illustrate effectiveness of the proposed methods.
1

3
12


S. H.
Hashemipour
Department of Electrical Engineering, Roudsar and Amlash Branch, Islamic Azad University, Roudsar, Iran.
Department of Electrical Engineering, Roudsar
Iran
c.e.hashemi@gmail.com


N.
Vasegh
Department of Electrical Engineering, ShahidRajaee Teacher Training University, Tehran, Iran.
Department of Electrical Engineering, ShahidRajaee
Iran
n.vasegh@srttu.edu


A.
Khaki Sedigh
Department of Electrical Engineering, K. N Toosi University of Technology, Tehran, Iran.
Department of Electrical Engineering, K.
Iran
sedigh@kntu.ac.ir
Interconnected system
MRAC
State and input delays
[[1] C. He, J. Li, L. Zhang, Decentralized adaptive control of nonlinear large.scale pure.feedback interconnected systems with time.varying delays, International Journal of Adaptive Control and Signal Processing, 29(1) (2015) 2440. ##[2] Z. Hu, Decentralized Stabilization of Large Scale Interconnected Systems with Delays, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 39 (1994). ##[3] L.N. Lv, Z.Y. Sun, X.J. Xie, Adaptive control for high.order time.delay uncertain nonlinear system and application to chemical reactor system, International Journal of Adaptive Control and Signal Processing, 29(2) (2015) 224241. ##[4] B. Mirkin, P.O. Gutman, Y. Shtessel, Decentralized continuous MRAC with local asymptotic sliding modes of nonlinear delayed interconnected systems, Journal of the Franklin Institute, 351(4) (2014) 20762088. ##[5] J.L. ChangChun Hua, XinPing Guan, Decentralized MRAC for largescale interconnected systems with timevarying delays and applications to chemical reactor systems, Journal of Process Control, (2012). ##[6] B. Mirkin, P.O. Gutman, Adaptive following of perturbed plants with input and state delays, in: Control and Automation (ICCA), 2011 9th IEEE International Conference on, IEEE, 2011, pp. 865870. ##[7] J.Y. H. Yau, Robust decentralized adaptive control for uncertain largescale delayed systems with input nonlinearity, Chaos, Solitons and Fractals, (2009) 1515 1521. ##[8] S.S. X. Yan, C.Edwards, Global timedelay dependent decentralized sliding mode control using only output information, in: 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, China, 2009, pp. 67096714. ##[9] H. Wu, Decentralized adaptive robust tracking and model following for large–scale systems including delayed state perturbations in the interconnections, Journal of Optimization Theory and Applications, (2008) 231253. ##[10] H. Wu, Decentralized adaptive robust control of uncertain largescale nonlinear dynamical systems with timevarying delays, IET Control Theory and Application, 6(5) (2012) 629640. ##[11] X.G. Changchun Hua, Peng Shib, Decentralized robust model reference adaptive control for interconnected timedelay systems, Journal of Computational and Applied Mathematics (2006) 383396. ##[12] H. Wu, M. Deng, Robust adaptive control scheme for uncertain nonlinear model reference adaptive control systems with timevarying delays, IET Control Theory and Applications, 9(8) (2015) 11811189. ##[13] O.J.M. Smith, A controller to overcome dead time, ISA Journal, (1959) 2833. ##[14] A.W.O. A. Z. Manitius, Finite spectrum assignment problem for systems with delays, IEEE Transactions on Automatic Control, (1979) 541553. ##[15] S.A. AlShamali, O.D. Crisalle, H.A. Latchman, An approach to stabilize linear systems with state and input delay, in: American Control Conference, 2003. Proceedings of the 2003, IEEE, 2003, pp. 875880. ##[16] Z.L.a.H. Fang, On asymptotic stability of linear systems with delayed input, IEEE Transaction on Automatic Control, 52 (2007) 9981013. ##[17] Z. Lin, Low Gain Feedback. London, UK: Springer, 1988. ##[18] Z.L. B. Zhou, G. Duan, Truncated predictor feedback for linear systems with long timevarying input delays, Automatica, (2012) 23872399. ##[19] B.M.a.P.O. Gutman, Adaptive Following of Perturbed Plants with Input and State Delays, in:9th IEEE International Conference on Control and Automation (ICCA) Santiago, Chile, 2011.##]
Integration Scheme for SINS/GPS System Based on Vertical Channel Decomposition and InMotion Alignment
2
2
Accurate alignment and vertical channel instability play an important role in the strapdown inertial navigation system (SINS), especially in the case that precise navigation has to be achieved over long periods of time. Due to poor initialization and the cumulative errors of lowcost inertial measurement units (IMUs), initial alignment is insufficient to achieve required navigation accuracy. To tackle this problem, in this paper, misalignment error is dynamically modeled and inmotion alignment is provided based on position and velocity matching. It is revealed that using misalignment error, orientation estimation can be properly corrected. Moreover, to prevent the instability effects of the vertical channel, decomposed SINS error model is derived. In the decomposed SINS error model, the navigation states in the vertical channel are separated from those in the horizontal plane. Twostep estimation process is developed for integration of the aforementioned SINS error dynamics with the measurements provided by global positioning system (GPS), and fifteenstate SINS/GPS mechanization is presented. Assessment of the proposed approach is conducted in the airborne test.
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13
22


H.
Nourmohammadi
Department of Mechanical Engineering, Tabriz University, Tabriz, Iran
Department of Mechanical Engineering, Tabriz
Iran
hnourmohammadi@tabrizu.ac.ir


J.
Keighobadi
Department of Mechanical Engineering, Tabriz University, Tabriz, Iran
Department of Mechanical Engineering, Tabriz
Iran
keighobadi@tabrizu.ac.ir
Lowcost Navigation
SINS/GPS Algorithm
InMotion Alignment
Vertical Channel Decomposition
[[1] Z. Ding, H. Cai, H. Yang, An improved multiposition calibration method for low cost microelectro mechanical systems inertial measurement units, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 229(10) (2015) 19191930. ##[2] N. ElSheimy, S. Nassar, A. Noureldin, Wavelet denoising for IMU alignment, IEEE Aerospace and Electronic Systems Magazine, 19(10) (2004) 3239. ##[3] J. Ali, M. Ushaq, A consistent and robust Kalman filter design for inmotion alignment of inertial navigation system, Journal of Measurement, 42(4) (2009) 577582. ##[4] B.H. Kaygisiz, I. Erkmen, A.M. Erkmen, GPS/INS enhancement for land navigation using neural network, Journal of Navigation, 57(02) (2004) 297310. ##[5] Y. Hao, Z. Xiong, Z. Hu, Particle filter for INS inmotion alignment, in: 1st IEEE Conference on Industrial Electronics and Applications, IEEE, pp. 16, 2006. ##[6] Q. Wang, Y. Li, K. Wang, C. Rizos, S. Li, The observability analysis and SPKF for the inmotion alignment of the looselyintegrated GPS/INS system, in: Proceedings of the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation, ION GNSS, pp. 104110, 2009. ##[7] R. Stancic, S. Graovac, The integration of strapdown INS and GPS based on adaptive error damping, Robotics and Autonomous Systems, 58(10) (2010) 11171129. ##[8] P. Doostdar, J. Keighobadi, Design and implementation of SMO for a nonlinear MIMO AHRS, Mechanical Systems and Signal Processing, 32 (2012) 94115. ##[9] Q. Li, Y. Ben, F. Sun, A novel algorithm for marine strapdown gyrocompass based on digital filter, Journal of Measurement, 46(1) (2013) 563571. ##[10] W. Li, J. Wang, L. Lu, W. Wu, A novel scheme for DVLaided SINS inmotion alignment using UKF techniques, Sensors, 13(1) (2013) 10461063. ##[11] T. Liu, Q. Xu, Y. Li, Adaptive filtering design for inmotion alignment of INS, in: Control and Decision Conference (2014 CCDC), The 26th Chinese, IEEE, 2014, pp. 26692674. ##[12] N. Musavi, J. Keighobadi, Adaptive fuzzy neuroobserver applied to low cost INS/GPS, Journal of Applied Soft Computing, 29 (2015) 8294. ##[13] H. Milanchian, J. Keighobadi, H. Nourmohammadi, Magnetic Calibration of ThreeAxis Strapdown Magnetometers for Applications in Mems Attitude Heading Reference Systems, AUT Journal of Modeling and Simulation, 47(1) (2015) 5565. ##[14] Y. Meng, S. Gao, Y. Zhong, G. Hu, A. Subic, Covariance matching based adaptive unscented Kalman filter for direct filtering in INS/GNSS integration, Journal of Acta Astronautica, 120 (2016) 171181. ##[15] H. Nourmohammadi, J. Keighobadi, Decentralized INS/GNSS System With MEMSGrade Inertial Sensors Using QRFactorized CKF, IEEE Sensors Journal, 17(11) (2017) 32783287. ##[16] D. Titterton, J.L. Weston, Strapdown inertial navigation technology, IET, 2004. ##[17] O.S. Salychev, Applied Inertial Navigation: problems and solutions, BMSTU Press Moscow, Russia, 2004. ##[18] R.M. Rogers, Applied mathematics in integrated navigation systems, Aiaa, 2003. ##[19] M. ElDiasty, S. Pagiatakis, Calibration and stochastic modelling of inertial navigation sensor errors, Journal of Global Positioning Systems, 7(2) (2008) 170182. ##[20] X.Y. Chen, J. Yu, X.F. Zhu, Theoretical analysis and application of Kalman filter for ultratight global position system/inertial navigation system integration, Transactions of the Institute of Measurement and Control, 34(5) (2011) 648662. ##]
Design of ObserverBased H∞ Controller for Robust Stabilization of Networked Systems Using Switched Lyapunov Functions
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2
In this paper, a H. controller is synthesized for networked systems subject to random transmission delays with known upper bound and different occurrence probabilities in both feedback (sensor to controller) and forward (controller to actuator) channels. A remote observer is employed to improve the performance of the system by computing nondelayed estimates of the states. The closedloop system is described in the framework of switched systems; then, a switched Lyapunov function is utilized to obtain conditions to determine the gains of the observer and the controller such that robust asymptotic stability of the system is assured. Two illustrative examples are presented to demonstrate the realworld applicability and superiority of the proposed approach compared to rival ones in the literatue.
1

23
30


A.
Farnam
SYSTEMS Research Group, Ghent University, Ghent, Belgium
SYSTEMS Research Group, Ghent University,
Belgium
arash.farnam@ugent.be


R.
Mahboobi Esfanjani
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
Department of Electrical Engineering, Sahand
Iran
mahboobi@sut.ac.ir
Networked Control System
H∞ controller
State observer
Random delays
Switched Lyapunov functions
[[1] L. Zhang, H. Gao, O. Kaynak, Networkinduced constraints in networked control systemsa survey, IEEE transactions on industrial informatics, 9(1) (2013) 403 416. ##[2] J.P. Hespanha, P. Naghshtabrizi, Y. Xu, A survey of recent results in networked control systems, Proceedings of the IEEE, 95(1) (2007) 138162. ##[3] Y. Tipsuwan, M.Y. Chow, Control methodologies in networked control systems, Control engineering practice, 11(10) (2003) 10991111. ##[4] A. Farnam, R.M. Esfanjani, Improved stabilization method for networked control systems with variable transmission delays and packet dropout, ISA transactions, 53(6) (2014) 17461753. ##[5] S. Kim, P. Park, C. Jeong, Robust H∞ stabilisation of networked control systems with packet analyser, IET control theory & applications, 4(9) (2010) 18281837. ##[6] F. Yang, Z. Wang, Y. Hung, M. Gani, H∞ control for networked systems with random communication delays, IEEE Transactions on Automatic Control, 51(3) (2006) 511518. ##[7] P. Seiler, R. Sengupta, An H∞ approach to networked control, IEEE Transactions on Automatic Control, 50(3) (2005) 356364. ##[8] H. Li, Z. Sun, H. Liu, M.Y. Chow, Predictive observer‐based control for networked control systems with network‐induced delay and packet dropout, Asian journal of control, 10(6) (2008) 638650. ##[9] H. Liu, Y. Shen, X. Zhao, Delaydependent observerbased H∞ finitetime control for switched systems with timevarying delay, Nonlinear Analysis: Hybrid Systems, 6(3) (2012) 885898. ##[10] G.P. Liu, Y. Xia, J. Chen, D. Rees, W. Hu, Networked predictive control of systems with random network delays in both forward and feedback channels, IEEE Transactions on Industrial Electronics, 54(3) (2007) 12821297. ##[11] G.P. Liu, Y. Xia, D. Rees, W. Hu, Design and stability criteria of networked predictive control systems with random network delay in the feedback channel, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(2) (2007) 173184. ##[12] R. Wang, B. Wang, G.P. Liu, W. Wang, D. Rees, H∞ controller design for networked predictive control systems based on the average dwelltime approach, IEEE Transactions on Circuits and Systems II: Express Briefs, 57(4) (2010) 310314. ##[13] R. Wang, G.P. Liu, W. Wang, D. Rees, Y.B. Zhao, H∞ Control for Networked Predictive Control Systems Based on the Switched Lyapunov Function Method, IEEE transactions on industrial electronics, 57(10) (2010) 35653571. ##[14] H. Gao, X. Meng, T. Chen, Stabilization of networked control systems with a new delay characterization, IEEE Transactions on Automatic Control, 53(9) (2008) 2142 2148. ##[15] C. Peng, D. Yue, E. Tian, Z. Gu, A delay distribution based stability analysis and synthesis approach for networked control systems, Journal of the Franklin Institute, 346(4) (2009) 349365. ##[16] P. Gahinet, A. Nemirovski, A. Laub, M. Chilali, LMI Control Toolbox User’s Guide. Natick, The MathWorks, Inc. P. Gahinet, (1995).##]
Robust Adaptive Control of Voltage Saturated Flexible Joint Robots with Experimental Evaluations
2
2
This paper is concerned with the problem of designing and implementing a robust adaptive control strategy for the flexible joint electrically driven robots (FJEDR) while considering the constraints on the actuator voltage input. The control design procedure is based on the function approximation technique, to avoid saturation besides being robust against both structured and unstructured uncertainties associated with external disturbances and unmodeled dynamics. Stability proof of the overall closedloop system is given via the Lyapunov direct method. The analytical studies as well as experimental results obtained using MATLAB/SIMULINK external mode control on a singlelink flexible joint electrically driven robot, demonstrate a high performance of the proposed control schemes.
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31
38


A.
Izadbakhsh
Department of Electrical Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran
Department of Electrical Engineering, Garmsar
Iran
izadbakhsh_alireza@hotmail.com
Robust Adaptive Control
realtime Implementation
Actuator saturation
Function approximation technique
[[1] A. Izadbakhsh, A. Akbarzadeh Kalat, M.M. Fateh, S.M.R. Rafiei, A robust AntiWindup control design for electrically driven robotsTheory and Experiment, International Journal of Control. Automation, and Systems, 9(5) (2011) 10051012. ##[2] A. Izadbakhsh. Robust control design for rigidlink flexiblejoint electrically driven robot subjected to constraint: theory and experimental verification, Nonlinear Dynamics, 85(2) (2016) 751765. ##[3] W. Gao, R.R. Selmic, Neural Network Control of a Class of Nonlinear Systems with Actuator Saturation, IEEE Transactions on Neural Networks, 17(1) (2006) 147156. ##[4] W. Peng, Z. Lin, J. Su, Computed torque controlbased composite nonlinear feedback controller for robot manipulators with bounded torques, IET Control Theory and Applications, 3(6) (2009) 701–711. ##[5] A. ZRio, V. Santibanez, Simple extensions of the PD with gravity compensation control law for robot manipulators with bounded inputs, IEEE Transactions on Control Systems Technology, 14(5) (2006) 958965. ##[6] A. ZRio, V. Santibanez, A natural saturating extension of the PD with desired gravity compensation control law for robot manipulators with bounded inputs, IEEE Transactions on Robotics, 23(2) (2007) 386391. ##[7] E. AguinagaRuiz, A. ZavalaRio, 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. ##[8] J. A. Ramirez, V. Santibanez, R. Campa, Stability of robot manipulators under Saturated PID compensation, IEEE Transactions on Control Systems Technology, 16(6) (2008) 13331341. ##[9] V. Santibanez, K. Camarillo, J. M. Valenzuela, R. Campa, A practical PID regulator with bounded torques for robot manipulators, International Journal of Control, Automation, and Systems, 8(3) (2010) 544555. ##[10] A. Izadbakhsh, M. M. Fateh, Robust Lyapunovbased control of flexiblejoint robots using voltage control strategy, Arabian journal for science and Engineering, 39(4) (2014) 31113121. ##[11] W. P. Li, B. Luo, H. Huang, Active vibration control of Flexible Joint Manipulator using Input Shaping and Adaptive Parameter Auto Disturbance Rejection Controller, Journal of Sound and Vibration, 363(17) (2016) 97–125. ##[12] A. M. Annaswamy, J. E. Wong, Adaptive control in the presence of saturation nonlinearity, International Journal of Adaptive Control and Signal Processing, 11(1) (1997) 319. ##[13] S. Purwar, I. N. Kar, A. N.Jha, Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints, Fuzzy Sets and Systems, 152(3) (2005) 651 664. ##[14] R. J. Caverly, D. E. Zlotnik, L. J. Bridgeman, J. R. Forbes, Saturated proportional derivative control of flexiblejoint manipulators, Robotics and Computer Integrated Manufacturing, 30(6) (2014) 658–666. ##[15] R. J. Caverly, D. E. Zlotnik, J. R. Forbes, Saturated control of flexiblejoint manipulators using a Hammerstein strictly positive real compensator, Robotica, 34(6) (2016) 13671382. ##[16] W. E. Dixon, Adaptive regulation of amplitude limited for robot manipulators with uncertain kinematics and dynamics, IEEE Transactions on Automatic Control, 52(3) (2007) 488493. ##[17] Z. Liu, J. Liu, W. He, Partial differential equation boundary control of a flexible manipulator with input saturation, International Journal of Systems Science, 48(1) (2017) 5362. ##[18] A. Izadbakhsh, M. Masoumi, FATbased robust adaptive control of flexiblejoint robots: singular perturbation approach, IEEE Industrial Society’s 18th International Conference on Industrial Technology (ICIT), 2017, pp. 803808. ##[19] Z. Qu, D. M. Dawson, Robust tracking control of robot manipulators, IEEE Press, Inc., New York, 1996. ##[20] K. S. Narendra, A. M. Annaswamy, Stable adaptive systems, Prentice Hall, Engle wood cliffs, NJ, 1989. ##[21] W. Gao. RR. Selmic, Adaptive Neural Network output feedback Control of Nonlinear Systems with Actuator Saturation, 44th IEEE Conference on Decision and Control, 2005, pp. 55225527. ##[22] A. Izadbakhsh, M. M. Fateh, Realtime robust adaptive control of robots subjected to actuator voltage constraint, Nonlinear Dynamics, 78(3) (2014) 19992014. ##[23] Anch. Huang, MCh. Chen, Adaptive control of robot manipulatorsA unified regressor free approach, World scientific, 2010. ##[24] A. Izadbakhsh, Closedform dynamic model of Puma560 robot arm, Proceedings of the 4th International Conf. on Autonomous Robots and Agents, 2009, pp. 675 680. ##[25] A. Izadbakhsh, A note on the nonlinear control of electrical flexiblejoint robots, Nonlinear Dynamics, 89(4) (2017) 27532767.##]
An Efficient Data Replication Strategy in LargeScale Data Grid Environments Based on Availability and Popularity
2
2
The data grid technology, which uses the scale of the Internet to solve storage limitation for the huge amount of data, has become one of the hot research topics. Recently, data replication strategies have been widely employed in a distributed environment to copy frequently accessed data in suitable sites. The primary purposes are shortening distances of the file transmission and achieving files from nearby locations to requested sites so as to minimize retrieval time and bandwidth usage. In this paper, we propose a new replica selection strategy which is based on response time and security. However, replication should be used wisely because the storage size of each Data Grid site is limited. In addition, we propose a new replica replacement strategy that considers file availability, time of access, access frequency and size of file. The simulation results report that the proposed strategy can effectively improve mean job time, bandwidth consumption for data delivery, and data availability compared with those of the tested algorithms.
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39
50


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
javid@uk.ac.ir
Data Grid
Dynamic replication
File access pattern
Job Scheduling
[[1] A. Chervenak, I. Foster, C. Kesselman, C. Salisbury, S. Tuecke, The data grid: towards an architecture for the distributed management and analysis of large scientific datasets, Journal of Network and Computer Applications, 23 (2001) 187200. ##[2] N. Rathore, I. Chana, Variable thresholdbased hierarchical load balancing technique in Grid, Engineering with Computers, 31(3) (2014) 597615. ##[3] A.S. Saleh, An efficient systemoriented grid scheduler based on a fuzzy matchmaking approach, Engineering with Computers, 29 (2013) 185206. ##[4] T. Hamrouni, S. Slimani, F. Ben Charrada, A survey of dynamic replication and replica selection strategies based on data mining techniques in data grids, Engineering Applications of Artificial Intelligence, 48 (2016) 140 158. ##[5] E. Gallicchio, J. Xia, W.F. Flynn, B. Zhang, S. Samlalsingh, A. Mentes, R.M. Levy, Asynchronous replica exchange software for grid and heterogeneous computing, Computer Physics Communications, 196 (2015) 236246. ##[6] S. Warhade, P. Dahiwale, M.M. Raghuwanshi, A dynamic data replication in grid system, in: 1st International Conference on Information Security & Privacy, 2016, 537543. ##[7] T. Hamrouni, S. Slimani, Faouzi Ben Charrada, A data mining correlated patternsbased periodic decentralized replication strategy for data grids, Journal of Systems and Software, 110 (2015) 1027. ##[8] E.U. Munir, J. Li, S. Shi, QoS suffrage heuristic for independent task scheduling in grid, Journal of Information Technology, 6 (2007)11661170. ##[9] OptorSim–A Replica Optimizer Simulation: http://edgwp2.web.cern.ch/edgwp2/ optimization/optorsim.html ##[10] S. Goel, R. Buyya, Data replication strategies in widearea distributed systems, Enterprise Service Computing: From Concept to Deployment, Idea Group Inc., Hershey, (2006) 211241. ##[11] Y. Saito, M. Shapiro, Optimistic replication, ACM Computing Surveys, 37(1) (2005) 4281. ##[12] I. Foster, K. Ranganathan, Design and evaluation of dynamic replication strategies for high performance data grids, in: Proceedings of International Conference on Computing in High Energy and Nuclear Physics, 2001. ##[13] I. Foster, K. Ranganathan, Identifying dynamic replication strategies for high performance data grids, in: Proceedings of 3rd IEEE/ACM International Workshop on Grid Computing, 2002, pp. 7586. ##[14] I. Foster, K. Ranganathan, Decoupling computation and data scheduling in distributed dataintensive applications, in: Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing, HPDC11, IEEE, CS Press, Edinburgh, UK, 2002, pp. 352358. ##[15] M. Bsoul, A. AlKhasawneh, E.E. Abdallah, Y. Kilani, Enhanced fast spread replication strategy for data grid, Journal of Network and Computer Applications, 34 (2011) 575580. ##[16] K. Sashi, A.S. Thanamani, Dynamic replica management for data grid, IACSIT International Journal of Engineering and Technology, 2 (2010) 329333. ##[17] R.S. Chang, H.P. Chang, A Dynamic data replication strategy using accessweight in data grids, The Journal of Supercomputing, 45 (2008) 277295. ##[18] S.M. Park, J.H. Kim, Y.B. Ko, W.S. Yoon, Dynamic grid replication strategy based on internet hierarchy, in: International Workshop on Grid and Cooperative Computing, 1001 (2003) 13241331. ##[19] K. Sashi, A.S. Thanamani, Dynamic replication in a data grid using a Modified BHR region based algorithm, Future Generation Computer Systems, 27 (2011) 202 210. ##[20] A. Horri, R. Sepahvand, G.H. Dastghaibyfard, A hierarchical scheduling and replication strategy, International Journal of Computer Science and Network Security, 8 (2008). ##[21] N. Mansouri, G.H. Dastghaibyfard, Job scheduling and dynamic data replication in data grid environment, The Journal of Supercomputing, 64 (2013) 204225. ##[22] R. Chang, J. Chang, S. Lin, Job scheduling and data replication on data grids, Future Generation Computer Systems, 23 (2007) 846860. ##[23] N. Mansouri, G.H. Dastghaibyfard, A dynamic replica management strategy in data grid, Journal of Network and Computer Applications, 35 (2012) 12971303. ##[24] N. Mansouri, G.H. Dastghaibyfard, E. Mansouri, Combination of data replication and scheduling algorithm for improving data availability in Data Grids, Journal of Network and Computer Applications, 36 (2013) 711722. ##[25] N. Mansouri, G.H. Dastghaibyfard, Enhanced dynamic hierarchical replication and weighted scheduling strategy in data grid, Journal of Parallel and Distributed Computing, 73 (2013) 534543. ##[26] C. Wang, C. Hsu, P. Liu, H. Chen, J. Wu, Optimizing server placement in hierarchical grid environments, The Journal of Supercomputing, 42 (2007) 267282. ##[27] C. Yang, C. Fu, C. Hsu, File replication, maintenance, and consistency management services in data grids, The Journal of Supercomputing, 53 (2010) 411439. ##[28] R.M. Rahman, R. Alhajj, K. Barker, Replica selection strategies in data grid, Journal of Parallel and Distributed Computing, 68 (2008) 15611574. ##[29] R. Vingralek, Y. Breitbart, M. Sayal, P. Scheuermann, Web++: a system for fast and reliable web service, in: Proceedings of the USENIX Annual Technical Conference, 1999. ##[30] M. Sayal, Y. Breitbart, P. Scheuermann, R. Vingralek, Selection algorithms for replicated web servers, in: Proceedings of the Workshop on Internet Server Performance, 1998. ##[31] Load Balancing System, Chapter 6 in Intel Solutions Manual, Intel Corporation, 4967. ##[32] R. M. Almuttairi, R. Wankar, A. Negi, R. Rao Chillarige, M.S. Almahna, New replica selection technique for binding replica sites in data grids, in: 1st International Conference on Energy, Power and Control (EPCIQ), 2010, pp. 187194. ##[33] S. Lewontin, E. Martin, Client side load balancing for the web, in: Proceedings of 6th International World Wide Web Conference, 1997, pp. 711. ##[34] Z. Fei, S. Bhattacharjee, E. Zegura, M. Ammar, A novel server selection technique for improving response time of a replicated service, in: Proceedings IEEE INFOCOM, 1998, pp. 783791. ##[35] G. Bingxiang ,Y. Kui, a global dynamic scheduling with replica selection algorithm using GridFTP, in: International Conference on Challenges in Environmental Science and Computer Engineering, 2010, pp. 106109. ##[36] M. Sayal, Y. Breitbart, P. Scheuermann, R. Vingralek, Selection algorithms for replicated web servers, in: Proceedings of the Workshop on Internet Server Performance, Wisconsin, 1998. ##[37] T. Ceryen, M. Kevin, Performance characterization of decentralized algorithms for replica selection in distributed object systems, in: Proceedings of the 5th International Workshop on Software Performance, 2005, pp. 257262. ##[38] B. Kusý, P. Dutta, P. Levis, Elapsed time on arrival: a simple and versatile primitive for canonical time synchronization services, Int. J. Ad Hoc and Ubiquitous Computing, 1 (2006) 114. ##[39] H. Hamad, E. ALMistarihi, C. Huah Yong, Response time optimization for replica selection service in data grids, Journal of Computer Science, 4 (2008) 487493. ##[40] D.G. Cameron, R. Carvajalschiaffino, A. Paul Millar, C. Nicholson, K. Stockinger, F. Zini, UK Grid Simulation with OptorSim, UK eScience All Hands Meeting, (2003). 49.##]
Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks
2
2
In this paper, dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC, the chattering is removed due to the integrator placed before the input control signal of the plant. However, in DSMC, the augmented system has higher order than the actual system, i.e. the states number of the augmented system is higher than the actual system and then to control of such a system, we must know and identify the new states, or the plant model should be completely known. To solve this problem, we suggest two online neural networks to identify and to obtain a model for the unknown nonlinear system. In the first approach, the neural network training law is based on the available system states and the bound of the observer error is not proved to converge to zero. The advantage of the second training law is only using the system’s output and the observer error converges to zero based on the Lyapunov stability theorem. To verify these approaches, DuffingHolmes chaotic systems (DHC) are used.
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51
60


A.
KaramiMollaee
Faculty of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran
Faculty of Electrical and Computer Engineering,
Iran
a_k_mollaee@yahoo.com


H.
Shanechi
Faculty of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran.
Faculty of Electrical and Computer Engineering,
Iran
shanechi@iit.edu
Dynamic Sliding Mode Control
Neural Model
Nonlinear system
DuffingHolmes Chaotic System
[[1] J.J. E. Slotine, W. Li, Applied nonlinear control, PrenticeHall, 1991. ##[2] H. Lee, V.I. Utkin, Chattering suppression methods in sliding mode control systems, Elsevier, Annual Review in Control, 31 (2007) 179188. ##[3] A. KaramiMollaee, N. Pariz, H. M. Shanechi, Position control of servomotors using neural dynamic sliding mode, Transactions of the ASME (American Society of Mechanical Engineering), Journal of Dynamic Systems, Measurement and Control, 133 (6) (2011) 141150. ##[4] W. Perruquetti, J. PierreBarbot, Sliding mode control in engineering, Marcel Dekker, 2002. ##[5] T. Sun, H. Pei, Y. Pan, H. Zhou, C. Zhang, Neural networkbased sliding mode adaptive control for robot manipulators, Elsevier, Neurocomputing, 74(1415) (2011) 23772384. ##[6] M.J. Zhang, Z.Z. Chu, Adaptive sliding mode control based on local recurrent neural networks for underwater robot, Elsevier, Ocean Engineering, 45 (2012) 5662. ##[7] Y. Zou, X. Lei, A compound control method based on the adaptive neural network and sliding mode control for inertial stable platform, Elsevier, Neurocomputing, 155 (2015) 286294. ##[8] S. Mefoued, A second order sliding mode control and a neural network to drive a knee joint actuated orthosis, Elsevier, Neurocomputing, 155 (2015) 7179. ##[9] H. M. Kim, S. H. Park, S. I. Han, Precise friction control for the nonlinear friction system using the friction state observer and sliding mode control with recurrent fuzzy neural networks, Elsevier, Mechatronics, 19 (2009) 805 815. ##[10] A. Levant, Sliding order and sliding accuracy in sliding mode control, International Journal of Control, 58 (1993) 12471263. ##[11] G. Bartolini, A. Ferrara, E. Usai, Chattering avoidance by secondorder sliding mode control, IEEE Transaction on Automatic Control, 43(2) (1998) 241246. ##[12] A. Levant, Robust exact differentiation via sliding mode techniques, Elsevier, Automatica, 34 (1998) 379384. ##[13] M. Norgaard, O. Ravn, N. K. Poulsen, L. K. Hansen, Neural network for modeling and control of dynamic systems, Springer, New York, 2001. ##[14] C.H. Lin, Recurrent wavelet neural network control of a PMSG system based on a PMSM wind turbine emulator, Turkish Journal of Electrical Engineering & Computer Sciences, 22(4) (2014) 795824. ##[15] O. Kaynak, K. Erbatur, R. Ertugrul, The fusion of computationally Intelligent methodologies and slidingmode control a survey, IEEE Transaction on Industrial Electronic, 48(1) (2001) 417. ##[16] M. K. Sifakis, S. J. Elliott, Strategies for the control of chaos in a Duffing–Holmes oscillator, Elsevier, Mechanical Systems and Signal Processing, 14(6) (2000) 9871002. ##[17] M. K. Sifakis, S. J. Elliott, Adaptive tracking control of DuffingHolmes chaotic systems with uncertainty, The 5th International Conference on Computer Science & Education, Hefei, China, August 24–27, 2010, pp. 1193 1197.##]
Kinematic and Dynamic Analyses of Tripteron, an OverConstrained 3DOF Translational Parallel Manipulator, through NewtonEuler Approach
2
2
In this research, as the main contribution, a comprehensive study is carried out on the mathematical modeling and analysis of the inverse kinematics and dynamics of an overconstrained three translational degreeoffreedom parallel manipulator. Due to inconsistency between the number of equations and the unknowns, the problem of obtaining the constraint forces and torques of overconstraint manipulators does not admit solution, which can be regarded as one of the drawbacks of such mechanisms. In this paper, in order to overcome this problem and circumvent inconsistency between the number of equations and the unknowns, two of the revolute joints attached to the endeffector are changed into a universal and a spherical joint without changing the motion pattern of the manipulator under study. Then, the dynamical equations of the manipulator are obtained based on the Newton–Euler approach, and a simple and a compact formulations are provided. Then, all the joint forces and torques are presented. In order to evaluate accuracy of the obtained formulated model, a motion for the endeffector as a case study is performed, and it has been shown that the results of the analytical model are in a good agreement with those obtained from SimMechanics model. Finally, the Root Mean Square error is calculated between the analytical model and the results obtained from the simulation and experimental study.
1

61
70


A.
Arian
Human and Robot Interaction Laboratory, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Human and Robot Interaction Laboratory, Faculty
Iran
aarian@ut.ac.ir


B.
Danaei
Human and Robot Interaction Laboratory, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Human and Robot Interaction Laboratory, Faculty
Iran
behzad.danaei@gmail.com


M.
Tale Masouleh
Human and Robot Interaction Laboratory, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Human and Robot Interaction Laboratory, Faculty
Iran
m.t.masouleh@ut.ac.ir
Decoupled parallel manipulator
Dynamic analysis
Kinematic analysis
Overconstraint manipulator
Newton–Euler approach
[[1] J.P. Merlet, C. Gosselin, Parallel mechanisms and robots, in: Springer Handbook of Robotics, Springer, 2008, pp. 269285. ##[2] L.W. Tsai, Robot analysis: the mechanics of serial and parallel manipulators, John Wiley & Sons, 1999 ##[3] M. Isaksson, T. Brogårdh, S. Nahavandi, Parallel manipulators with a rotationsymmetric arm system, Journal of mechanical design, 134(11) (2012) 114503. ##[4] M. Isaksson, T. Brogårdh, M. Watson, S. Nahavandi, P. Crothers, The Octahedral Hexarot—A novel 6DOF parallel manipulator, Mechanism and machine theory, 55 (2012) 91102 ##[5] M. Isaksson, A. Eriksson, M. Watson, T. Brogårdh, S. Nahavandi, A method for extending planar axissymmetric parallel manipulators to spatial mechanisms, Mechanism and Machine Theory, 83 (2015) 113 ##[6] C. Gosselin, Compact dynamic models for the tripteron and quadrupteron parallel manipulators, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 223(1) (2009) 112 ##[7] B. Danaei, A. Arian, M.T. Masouleh, A. Kalhor, Kinematic and Dynamic Modeling and Base Inertial Parameters Determination of the Quadrupteron Parallel Manipulator, in: Computational Kinematics, Springer, (2018), pp. 249256 ##[8] M.T. Masouleh, M.H. Saadatzi, C.m. Gosselin, H.D. Taghirad, A geometric constructive approach for the workspace analysis of symmetrical 5PRUR parallel mechanisms (3T2R), in: ASME Design Engineering Technical Conferences, (2010), pp. 1518 ##[9] C. Quennouelle, C. Gosselin, Kinematostatic modeling of compliant parallel mechanisms, Meccanica, 46(1) (2011) 155169 ##[10] C.M. Gosselin, M.T. Masouleh, V. Duchaine, P.L. Richard, S. Foucault, X. Kong, Parallel mechanisms of the multipteron family: kinematic architectures and benchmarking, in: Robotics and Automation, 2007 IEEE International Conference on, IEEE, (2007), pp. 555560 ##[11] X. Kong, C.M. Gosselin, Kinematics and singularity analysis of a novel type of 3CRR 3DOF translational parallel manipulator, The International Journal of Robotics Research, 21(9) (2002) 791798 ##[12] M. Sharifzadeh, M.T. Masouleh, A. Kalhor, On human–robot interaction of a 3DOF decoupled parallel mechanism based on the design and construction of a novel and lowcost 3DOF force sensor, Meccanica, 52(2017) 24712490 ##[13] R. Di Gregorio, V. ParentiCastelli, Dynamics of a class of parallel wrists, in: ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers, 2002, pp. 269277 ##[14] Y. Li, Q. Xu, Dynamic modeling and robust control of a 3PRC translational parallel kinematic machine, Robotics and ComputerIntegrated Manufacturing, 25(3) (2009) 630640 ##[15] B. Danaei, A. Arian, M.T. Masouleh, A. Kalhor, Dynamic modeling and base inertial parameters determination of a 2DOF spherical parallel mechanism, Multibody System Dynamics, 41(4) (2017) 367390 ##[16] H. Kalani, A. Rezaei, A. Akbarzadeh, Improved general solution for the dynamic modeling of Gough–Stewart platform based on principle of virtual work, Nonlinear Dynamics, 83(4) (2016) 23932418 ##[17] J. Wang, C.M. Gosselin, A new approach for the dynamic analysis of parallel manipulators, Multibody System Dynamics, 2(3) (1998) 317334 ##[18] A. Arian, B. Danaei, M.T. Masouleh, Kinematics and dynamics analysis of a 2DOF spherical parallel robot, in: Robotics and Mechatronics (ICROM), 2016 4th International Conference on, IEEE, 2016, pp. 154159 ##[19] Z. Bi, S. Lang, M. Verner, Dynamic modeling and validation of a tripodbased machine tool, The International Journal of Advanced Manufacturing Technology, 37(34) (2008) 410421 ##[20] Y.W. Li, J.S. Wang, L.P. Wang, X.J. Liu, Inverse dynamics and simulation of a 3DOF spatial parallel manipulator, in: Robotics and Automation, 2003. Proceedings. ICRA’03. IEEE International Conference on, IEEE, 2003, pp. 40924097 ##[21] T.D. Thanh, J. Kotlarski, B. Heimann, T. Ortmaier, On the inverse dynamics problem of general parallel robots, in: Mechatronics, 2009. ICM 2009. IEEE International Conference on, IEEE, 2009, pp. 16 ##[22] W. Do, D. Yang, Inverse dynamic analysis and simulation of a platform type of robot, Journal of Robotic Systems, 5(3) (1988) 209227 ##[23] C. Reboulet, T. Berthomieu, Dynamic models of a six degree of freedom parallel manipulators, in: Advanced Robotics, 1991.’Robots in Unstructured Environments’, 91 ICAR., Fifth International Conference on, IEEE, 1991, pp. 11531157 ##[24] A.M. Lopes, Dynamic modeling of a Stewart platform using the generalized momentum approach, Communications in Nonlinear Science and Numerical Simulation, 14(8) (2009) 33893401 ##[25] E. Zahariev, J. Cuadrado, Dynamics of overconstrained rigid and flexible multibody systems, in: 12th IFToMM World Congress, Besançon, France, 2007 ##[26] D. Gan, J.S. Dai, J. Dias, L. Seneviratne, Joint force decomposition and variation in unified inverse dynamics analysis of a metamorphic parallel mechanism, Meccanica, 51(7) (2016) 15831593 ##[27] Z. Bi, B. Kang, An inverse dynamic model of overconstrained parallel kinematic machine based on Newton–Euler formulation, Journal of Dynamic Systems, Measurement, and Control, 136(4) (2014) 041001 ##[28] C. Gosselin, Parallel computational algorithms for the kinematics and dynamics of planar and spatial parallel manipulators, Journal of Dynamic Systems, Measurement, and Control, 118(1) (1996) 2228 ##[29] X. Kong, C.M. Gosselin, Type synthesis of parallel mechanisms, Springer Publishing Company, Incorporated, 2007##]
Forecasting Gold Price Changes: Application of an Equipped Artificial Neural Network
2
2
The forecast of fluctuations of prices is the major concern in financial markets. Thus, developing an accurate and robust forecasting decision model is critical for investors. As gold has shown a special capability to smooth inflation fluctuations, governors use gold as a price controlling lever. Thus, more information about future gold price trends will help make the firm decisions. This paper attempts to propose an intelligent model founded by artificial neural networks (ANNs) to project future prices of gold. The proposed intelligent network is equipped with a metaheuristic algorithm called BAT algorithm to make ANN capable of following fluctuations. The designed model is compared to that of a published scientific paper and other competitive models such as Autoregressive Integrated Moving Average (ARIMA), ANN, Adaptive NeuroFuzzy Inference System (ANFIS), Multilayer Perceptron (MLP) Neural Network, Radial Basis Function (RBF) Neural Network and Generalized Regression Neural Networks (GRNN). In order to evaluate model performance, Root Mean Squared Error (RMSE) was employed as an error index. Results show that the proposed BATNeural Network (BNN) outperforms both conventional and modern forecasting models.
1

71
82


R.
Hafezi
Technology Foresight Group, Department of Management, Science and Technology, Amirkabir University of Technology, Tehran, Iran.
Technology Foresight Group, Department of
Iran
r.hafezi@aut.ac.ir


A.
Akhavan
Technology Foresight Group, Department of Management, Science and Technology, Amirkabir University of Technology, Tehran, Iran.
Technology Foresight Group, Department of
Iran
akhavan@aut.ac.ir
Artificial Intelligence
BAT Algorithm
Forecasting
Gold Price Fluctuations
Neural Network
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Zimbra, A dynamic artificial neural network model for forecasting time series events, International Journal of Forecasting, 21 (2005) 341362. ##[7] S. Mirmirani, H.C. Li, Gold Price, Neural Networks and Genetic Algorithm, Computational Economics, 23 (2004) 193–200. ##[8] A.e.L.S. Maia, F.d.A.T.d. Carvalho, Holt’s exponential smoothing and neural network models for forecasting intervalvalued time series, International Journal of Forecasting, 27 (2011) 740759. ##[9] T. Hida, Brownian motion, in: Brownian Motion, Springer, 1980, pp. 44113. ##[10] C. Park, W. Padgett, Accelerated degradation models for failure based on geometric Brownian motion and gamma processes, Lifetime Data Analysis, 11(4) (2005) 511527. ##[11] F.A. Postali, P. Picchetti, Geometric Brownian motion and structural breaks in oil prices: a quantitative analysis, Energy Economics, 28(4) (2006) 506522. ##[12] M.P. Taylor, D.A. Peel, L. Sarno, Nonlinear Mean. Reversion in Real Exchange Rates: Toward a Solution to the Purchasing Power Parity Puzzles, International economic review, 42(4) (2001) 10151042. ##[13] J.M. Poterba, L.H. Summers, Mean reversion in stock prices: Evidence and implications, Journal of financial economics, 22(1) (1988) 2759. ##[14] A. Kian, A. Keyhani, Stochastic price modeling of electricity in deregulated energy markets, in: System Sciences, 2001. Proceedings of the 34th Annual Hawaii International Conference on, IEEE, 2001, pp. 7 pp. ##[15] R.G. Haight, T.P. Holmes, Stochastic price models and optimal tree cutting: results for loblolly pine, (1991). ##[16] C. Blanco, D. Soronow, Jump diffusion processesenergy price processes used for derivatives pricing and risk management, Commodities now September 2001a, 2 (2001) 8387. ##[17] J. Lee, J.A. List, M.C. Strazicich, Nonrenewable resource prices: Deterministic or stochastic trends?, Journal of Environmental Economics and Management, 51(3) (2006) 354370. ##[18] S. Shafiee, E. Topal, Introducing a new model to forecast mineral commodity price, in: First International Future Mining Conference & Exhibition 2008, Australasian Institute of Mining and Metallurgy, 2008, pp. 243250. ##[19] M.A.G. Dias, K.M.C. Rocha, Petroleum concessions with extendible options using mean reversion with jumps to model oil prices, in: 3rd Real Options Conference, 1999, pp. 127. ##[20] D.G. Laughton, H.D. Jacoby, Reversion, timing options, and longterm decisionmaking, Financial Management, (1993) 225240. ##[21] S. Kazemi, E. Hadavandi, F. Mehmanpazir, M.M. Nakhostin, A hybrid intelligent approach for modeling brand choice and constructing a market response simulator, KnowledgeBased Systems, 40 (2013) 101 110. ##[22] M. AydinalpKoksal, V.I. Ugursal, Comparison of neural network, conditional demand analysis, and engineering approaches for modeling enduse energy consumption in the residential sector, Applied Energy, 85(4) (2008) 271 296. ##[23] Y. Shimoda, Y. Yamaguchi, T. Okamura, A. Taniguchi, Y. Yamaguchi, Prediction of greenhouse gas reduction potential in Japanese residential sector by residential energy enduse model, Applied Energy, 87(6) (2010) 19441952. ##[24] J.A. Rodger, A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings, Expert Systems with Applications, 41(4) (2014) 18131829. ##[25] R. Hafezi, A. Akhavan, A NOVEL CONCEPTUAL RISK MANAGEMENT MODEL BASED ON THE FUTURE’S UNCERTAINTIES, in: 8th International Scientific Conference “Business and Management, Vilnius, LITHUANIA, 2014. ##[26] M. Alipour, S. Alighaleh, R. Hafezi, M. Omranievardi, A new hybrid decision framework for prioritizing funding allocation to Iran’s energy sector, Energy, 121 (2017) 388402. ##[27] R. Hafezi, A. Akhavan, S. Pakseresht, Projecting plausible futures for Iranian oil and gas industries: Analyzing of historical strategies, Journal of Natural Gas Science and Engineering, 39 (2017) 1527. ##[28] M. Alipour, R. Hafezi, M. Amer, A. Akhavan, A new hybrid fuzzy cognitive mapbased scenario planning approach for Iran’s oil production pathways in the postesanction period, Energy, 135 (2017) 851e864. ##[29] C. Baumeister, L. Kilian, Realtime analysis of oil price risks using forecast scenarios, (2011). ##[30] Ö. Dilaver, Z. Dilaver, L.C. Hunt, What drives natural gas consumption in Europe? Analysis and projections, Journal of Natural Gas Science and Engineering, 19 (2014) 125136. ##[31] A. YazdaniChamzini, S.H. Yakhchali, D. Volungevičienė, E.K. Zavadskas, Forecasting gold price changes by using adaptive network fuzzy inference system, Journal of Business Economics and Management, 13(5) (2012) 9941010. ##[32] C. Liu, To Integrate Text Mining and Artificial Neural Network to Forecast Gold Futures Price, in: International Conference on Management and Service Science IEEE, 2009, pp. 1 4 ##[33] S. Zhou, K.K. Lai, An Improved EMD Online Learning Based Model for Gold Market Forecasting, Intelligent Decision Technologies, 10 (2011) 7584. ##[34] Wensheng Dai, ChiJie Lu, T. Chang, Empirical Research of Price Discovery for Gold Futures Based on Compound Model Combing Wavelet Frame with Support Vector Regression, Artificial Intelligence and Computational Intelligence, 6320 (2010) 374381. ##[35] F. Zhang, Z. Liao, Gold Price Forecasting Based on RBF Neural Network and Hybrid Fuzzy Clustering Algorithm, in: J. Xu, J.A. Fry, B. Lev, A. Hajiyev (Eds.) Proceedings of the Seventh International Conference on Management Science and Engineering Management, Springer Berlin Heidelberg, 2014, pp. 7384. ##[36] J. Kumar, T. Rao, S. Srivastava, Economics of Gold Price MovementForecasting Analysis Using Macroeconomic, Investor Fear and Investor Behavior Features, in: S. Srinivasa, V. Bhatnagar (Eds.) Big Data Analytics, Springer Berlin Heidelberg, 2012, pp. 111121. ##[37] C. Pierdzioch, M. Risse, S. Rohloff, A boosting approach to forecasting the volatility of goldprice fluctuations under flexible loss, Resources Policy, 47 (2016) 95107. ##[38] K. Gangopadhyay, A. Jangir, R. Sensarma, Forecasting the price of gold: An error correction approach, IIMB Management Review, 28(1) (2016) 612. ##[39] L. Xian, K. He, K.K. Lai, Gold price analysis based on ensemble empirical model decomposition and independent component analysis, Physica A: Statistical Mechanics and its Applications, 454 (2016) 1123. ##[40] W. Kristjanpoller, M.C. Minutolo, Gold price volatility: A forecasting approach using the Artificial Neural Network–GARCH model, Expert Systems with Applications, 42(20) (2015) 72457251. ##[41] D.G. Baur, J. Beckmann, R. Czudaj, A melting pot— Gold price forecasts under model and parameter uncertainty, International Review of Financial Analysis, 48 (2016) 282291. ##[42] H. Dehghani, M. Ataeepour, Determination of the effect of operating cost uncertainty on mining project evaluation, Resources Policy, 37(1) (2012) 109117. ##[43] H. Dehghani, M. Ataeepour, A. Esfahanipour, Evaluation of the mining projects under economic uncertainties using multidimensional binomial tree, Resources Policy, 39 (2014) 124133. ##[44] T. Kriechbaumer, A. Angus, D. Parsons, M.R. Casado, An improved wavelet–ARIMA approach for forecasting metal prices, Resources Policy, 39 (2014) 3241. ##[45] Y. Chen, K. He, C. Zhang, A novel grey wave forecasting method for predicting metal prices, Resources Policy, 49 (2016) 323331. ##[46] Y. Chen, Y. Zou, Y. Zhou, C. Zhang, Multistepahead Crude Oil Price Forecasting based on Grey Wave Forecasting Method, Procedia Computer Science, 91 (2016) 10501056. ##[47] D. Liu, Z. Li, Gold Price Forecasting and Related Influence Factors Analysis Based on Random Forest, in: Proceedings of the Tenth International Conference on Management Science and Engineering Management, Springer, 2017, pp. 711723. ##[48] C. Liu, Z. Hu, Y. Li, S. Liu, Forecasting copper prices by decision tree learning, Resources Policy, 52 (2017) 427434. ##[49] K.C. Sivalingam, S. Mahendran, S. Natarajan, Forecasting gold prices based on extreme learning machine, International Journal of Computers Communications & Control, 11(3) (2016) 372380. ##[50] B. Guha, G. Bandyopadhyay, Gold Price Forecasting Using ARIMA Model, Journal of Advanced Management Science Vol, 4(2) (2016). ##[51] R.K. Sharma, Forecasting Gold price with Box Jenkins Autoregressive Integrated Moving Average Method, Journal of International Economics, 7(1) (2016) 32. ##[52] X.S. Yang, A New Metaheuristic BatInspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer Berlin Heidelberg, 2010, pp. 65–74. ##[53] N.S. Jaddi, S. Abdullah, A.R. Hamdan, Optimization of neural network model using modified batinspired algorithm, Applied Soft Computing, 37 (2015) 7186. ##[54] R. Hafezi, J. Shahrabi, E. Hadavandi, A batneural network multiagent system (BNNMAS) for stock priceprediction: Case study of DAX stock price, Applied Soft Computing, 29 (2015) 196–210. ##[55] H. Dehghani, D. Bogdanovic, Copper price estimation using bat algorithm, Resources Policy, in press (2017). ##[56] R. Svečko, D. Kusić, Feedforward neural network position control of a piezoelectric actuator based on a BAT search algorithm, Expert Systems with Applications, 42(13) (2015) 54165423. ##[57] G. Zhang, B.E. Patuwo, M.Y. Hu, Forecasting with artificial neural networks:The state of the art, International Journal of Forecasting, 14(1) (1998) 35–62. ##[58] L.N. Trefethen, Spectral methods in MATLAB, SIAM, 2000. ##[59] P. Box, G.M. Jenkins, Time Series Analysis: Forecasting and Control, Holdenday Inc, San Francisco, CA, 1976. ##[60] G.S. Atsalakis, E.M. Dimitrakakis, C.D. Zopounidis, Elliott Wave Theory and neurofuzzysystems, in stock market prediction: the WASP system,, Expert Systems with Applications, 38 (2011) 9196–9206. ##[61] A.H. Fath, Application of radial basis function neural networks in bubble point oil formation volume factor prediction for petroleum systems, Fluid Phase Equilibria, (2017). ##[62] R.J. Schalkoff, Artificial neural networks, McGrawHill Higher Education, 1997. ##[63] D.F. Specht, A general regression neural network, IEEE transactions on neural networks, 2(6) (1991) 568576. ##[64] R. Hu, S. Wen, Z. Zeng, T. Huang, A shortterm power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm, Neurocomputing, 221 (2017) 2431. ##[65] I.A. Gheyas, L.S. Smith, Feature subset selection in large dimensionality domains, Pattern recognition, 43(1) (2010) 513. ##[66] J. Park, K.Y. Kim, Metamodeling using generalized regression neural network and particle swarm optimization, Applied Soft Computing, 51 (2017) 354 369. ##[67] A. Moghadassi, F. Parvizian, S. Hosseini, A new approach based on artificial neural networks for prediction of high pressure vaporliquid equilibrium, Australian Journal of Basic and Applied Sciences, 3(3) (2009) 18511862. ##[68] E. Heidari, M.A. Sobati, S. Movahedirad, Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLPANN), Chemometrics and Intelligent Laboratory Systems, 155 (2016) 7385.##]
Presenting a Model for MultipleStepAheadForecasting of Volatility and Conditional Value at Risk in Fossil Energy Markets
2
2
Fossil energy markets have always been known as strategic and important markets. They have a significant impact on the macro economy and financial markets of the world. The nature of these markets is accompanied by sudden shocks and volatility in the prices. Therefore, they must be controlled and forecasted using appropriate tools. This paper adopts the Generalized Auto Regressive Conditional Heteroskedasticity (GARCH)type models, Exponential Smoothing (ES)type models, and classic model in order to multiplestepahead forecast volatility, Value at Risk, and Conditional Value at Risk of Brent oil and natural gas in two different estimation window lengths, respectively. To evaluate the accuracy of the aforementioned models, eight different loss functions are utilized. There are a lot of financial terms in this the noted part. So, it’s comprehensible for financial person and etc. Therefore, the HWES model is proposed to multiplestepahead forecast functions as a verb.
1

83
94


E.
Mohammadian Amiri
Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
Faculty of Industrial Engineering, K. N.
Iran
emohammadian@email.kntu.ac.ir


S. B.
Ebrahimi
Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
Faculty of Industrial Engineering, K. N.
Iran
b_ebrahimi@kntu.ac.ir
Multiplestepahead Forecasting
Volatility
Value at Risk
Conditional Value at Risk
ES models
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Adaptive attitude controller of a reentry vehicles based on Backstepping Dynamic inversion method
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This paper presents an attitude control algorithm for a Reusable Launch Vehicle (RLV) with a low lift/drag ratio (L/D < 0.5), in presence of external disturbances, model uncertainties, control output constraints and the thruster model. The main novelty of the proposed control strategy is a new combination of the attitude control methods including backstepping, dynamic inversion, and adaptive control methods which will be called BacksteppingDynamic inversionAdaptive (B.D.A) method. In the proposed method, a single control variable is considered as the bank angle while the angle of the attack and the side slip angle will be stabilized in their inherent value. The purpose of this control is the attitude control of the vehicle to track the commanded bank angle and keep the vehicle in the desired trajectory. Lyapunov stability analysis of the closedloop system will be performed to guaranty the stability of the vehicle in the presence of constraints. Performance of the controller will be evaluated based on six Degrees of Freedom (6DOF) model of the reentry capsule. Also, the results of the proposed control algorithm will be compared with the Backstepping Dynamic inversion (B.D) control method.
1

95
106


A.
Mohseni
Department of Aerospace Engineering, Amirkabir University of Technology, 158754413, Tehran, Iran.
Department of Aerospace Engineering, Amirkabir
Iran
abdollah.mohseni@aut.ac.ir


F.
Fani Saberi
Space Science and Technology Institute, Amirkabir University of Technology, 158754413, Tehran, Iran.
Space Science and Technology Institute, Amirkabir
Iran
f.sabery@aut.ac.ir


M.
Mortazavi
Department of Aerospace Engineering, Amirkabir University of Technology, 158754413, Tehran, Iran.
Department of Aerospace Engineering, Amirkabir
Iran
mortazavi@aut.ac.ir
Attitude control
BacksteppingDynamic inversionAdaptive
Reusable Launch Vehicle (RLV)
Controller output constraint
Thruster model
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