ORIGINAL_ARTICLE
Decentralized Model Reference Adaptive Control of Large Scale Interconnected Systems with Both State and Input Delays
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 non-negative 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 large-scale time-delay 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.
https://miscj.aut.ac.ir/article_1012_428d19563f55bb816e80d0b1b51934d9.pdf
2018-06-01
3
12
10.22060/miscj.2017.12239.5021
Interconnected system
MRAC
State and input delays
S. H.
Hashemipour
c.e.hashemi@gmail.com
1
Department of Electrical Engineering, Roudsar and Amlash Branch, Islamic Azad University, Roudsar, Iran.
LEAD_AUTHOR
N.
Vasegh
n.vasegh@srttu.edu
2
Department of Electrical Engineering, ShahidRajaee Teacher Training University, Tehran, Iran.
AUTHOR
A.
Khaki Sedigh
sedigh@kntu.ac.ir
3
Department of Electrical Engineering, K. N Toosi University of Technology, Tehran, Iran.
AUTHOR
[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) 24-40.
1
[2] Z. Hu, Decentralized Stabilization of Large Scale Interconnected Systems with Delays, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 39 (1994).
2
[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) 224-241.
3
[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) 2076-2088.
4
[5] J.L. Chang-Chun Hua, Xin-Ping Guan, Decentralized MRAC for large-scale interconnected systems with time-varying delays and applications to chemical reactor systems, Journal of Process Control, (2012).
5
[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. 865-870.
6
[7] J.Y. H. Yau, Robust decentralized adaptive control for uncertain large-scale delayed systems with input nonlinearity, Chaos, Solitons and Fractals, (2009) 1515- 1521.
7
[8] S.S. X. Yan, C.Edwards, Global time-delay 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. 6709-6714.
8
[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) 231-253.
9
[10] H. Wu, Decentralized adaptive robust control of uncertain large-scale non-linear dynamical systems with time-varying delays, IET Control Theory and Application, 6(5) (2012) 629-640.
10
[11] X.G. Changchun Hua, Peng Shib, Decentralized robust model reference adaptive control for interconnected time-delay systems, Journal of Computational and Applied Mathematics (2006) 383-396.
11
[12] H. Wu, M. Deng, Robust adaptive control scheme for uncertain non-linear model reference adaptive control systems with time-varying delays, IET Control Theory and Applications, 9(8) (2015) 1181-1189.
12
[13] O.J.M. Smith, A controller to overcome dead time, ISA Journal, (1959) 28-33.
13
[14] A.W.O. A. Z. Manitius, Finite spectrum assignment problem for systems with delays, IEEE Transactions on Automatic Control, (1979) 541-553.
14
[15] S.A. Al-Shamali, 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. 875-880.
15
[16] Z.L.a.H. Fang, On asymptotic stability of linear systems with delayed input, IEEE Transaction on Automatic Control, 52 (2007) 998-1013.
16
[17] Z. Lin, Low Gain Feedback. London, UK: Springer, 1988.
17
[18] Z.L. B. Zhou, G. Duan, Truncated predictor feedback for linear systems with long time-varying input delays, Automatica, (2012) 2387-2399.
18
[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.
19
ORIGINAL_ARTICLE
Integration Scheme for SINS/GPS System Based on Vertical Channel Decomposition and In-Motion Alignment
Accurate alignment and vertical channel instability play an important role in the strap-down 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 low-cost 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 in-motion 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. Two-step estimation process is developed for integration of the aforementioned SINS error dynamics with the measurements provided by global positioning system (GPS), and fifteen-state SINS/GPS mechanization is presented. Assessment of the proposed approach is conducted in the airborne test.
https://miscj.aut.ac.ir/article_1013_8bf5846b4d932c3d0a9632092c83d208.pdf
2018-06-01
13
22
10.22060/miscj.2017.12483.5036
Low-cost Navigation
SINS/GPS Algorithm
In-Motion Alignment
Vertical Channel Decomposition
H.
Nourmohammadi
hnourmohammadi@tabrizu.ac.ir
1
Department of Mechanical Engineering, Tabriz University, Tabriz, Iran
AUTHOR
J.
Keighobadi
keighobadi@tabrizu.ac.ir
2
Department of Mechanical Engineering, Tabriz University, Tabriz, Iran
LEAD_AUTHOR
[1] Z. Ding, H. Cai, H. Yang, An improved multi-position calibration method for low cost micro-electro mechanical systems inertial measurement units, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 229(10) (2015) 1919-1930.
1
[2] N. El-Sheimy, S. Nassar, A. Noureldin, Wavelet de-noising for IMU alignment, IEEE Aerospace and Electronic Systems Magazine, 19(10) (2004) 32-39.
2
[3] J. Ali, M. Ushaq, A consistent and robust Kalman filter design for in-motion alignment of inertial navigation system, Journal of Measurement, 42(4) (2009) 577-582.
3
[4] B.H. Kaygisiz, I. Erkmen, A.M. Erkmen, GPS/INS enhancement for land navigation using neural network, Journal of Navigation, 57(02) (2004) 297-310.
4
[5] Y. Hao, Z. Xiong, Z. Hu, Particle filter for INS in-motion alignment, in: 1st IEEE Conference on Industrial Electronics and Applications, IEEE, pp. 1-6, 2006.
5
[6] Q. Wang, Y. Li, K. Wang, C. Rizos, S. Li, The observability analysis and SPKF for the in-motion alignment of the loosely-integrated GPS/INS system, in: Proceedings of the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation, ION GNSS, pp. 104-110, 2009.
6
[7] R. Stancic, S. Graovac, The integration of strap-down INS and GPS based on adaptive error damping, Robotics and Autonomous Systems, 58(10) (2010) 1117-1129.
7
[8] P. Doostdar, J. Keighobadi, Design and implementation of SMO for a nonlinear MIMO AHRS, Mechanical Systems and Signal Processing, 32 (2012) 94-115.
8
[9] Q. Li, Y. Ben, F. Sun, A novel algorithm for marine strapdown gyrocompass based on digital filter, Journal of Measurement, 46(1) (2013) 563-571.
9
[10] W. Li, J. Wang, L. Lu, W. Wu, A novel scheme for DVL-aided SINS in-motion alignment using UKF techniques, Sensors, 13(1) (2013) 1046-1063.
10
[11] T. Liu, Q. Xu, Y. Li, Adaptive filtering design for in-motion alignment of INS, in: Control and Decision Conference (2014 CCDC), The 26th Chinese, IEEE, 2014, pp. 2669-2674.
11
[12] N. Musavi, J. Keighobadi, Adaptive fuzzy neuro-observer applied to low cost INS/GPS, Journal of Applied Soft Computing, 29 (2015) 82-94.
12
[13] H. Milanchian, J. Keighobadi, H. Nourmohammadi, Magnetic Calibration of Three-Axis Strapdown Magnetometers for Applications in Mems Attitude- Heading Reference Systems, AUT Journal of Modeling and Simulation, 47(1) (2015) 55-65.
13
[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) 171-181.
14
[15] H. Nourmohammadi, J. Keighobadi, Decentralized INS/GNSS System With MEMS-Grade Inertial Sensors Using QR-Factorized CKF, IEEE Sensors Journal, 17(11) (2017) 3278-3287.
15
[16] D. Titterton, J.L. Weston, Strapdown inertial navigation technology, IET, 2004.
16
[17] O.S. Salychev, Applied Inertial Navigation: problems and solutions, BMSTU Press Moscow, Russia, 2004.
17
[18] R.M. Rogers, Applied mathematics in integrated navigation systems, Aiaa, 2003.
18
[19] M. El-Diasty, S. Pagiatakis, Calibration and stochastic modelling of inertial navigation sensor errors, Journal of Global Positioning Systems, 7(2) (2008) 170-182.
19
[20] X.-Y. Chen, J. Yu, X.-F. Zhu, Theoretical analysis and application of Kalman filter for ultra-tight global position system/inertial navigation system integration, Transactions of the Institute of Measurement and Control, 34(5) (2011) 648-662.
20
ORIGINAL_ARTICLE
Design of Observer-Based H∞ Controller for Robust Stabilization of Networked Systems Using Switched Lyapunov Functions
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 non-delayed estimates of the states. The closed-loop 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 real-world applicability and superiority of the proposed approach compared to rival ones in the literatue.
https://miscj.aut.ac.ir/article_1014_d2fcfe1539d8c5de64b9ddeb5559d79d.pdf
2018-06-01
23
30
10.22060/miscj.2017.12188.5013
Networked Control System
H∞ controller
State observer
Random delays
Switched Lyapunov functions
A.
Farnam
arash.farnam@ugent.be
1
SYSTEMS Research Group, Ghent University, Ghent, Belgium
AUTHOR
R.
Mahboobi Esfanjani
mahboobi@sut.ac.ir
2
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
LEAD_AUTHOR
[1] L. Zhang, H. Gao, O. Kaynak, Network-induced constraints in networked control systems-a survey, IEEE transactions on industrial informatics, 9(1) (2013) 403- 416.
1
[2] J.P. Hespanha, P. Naghshtabrizi, Y. Xu, A survey of recent results in networked control systems, Proceedings of the IEEE, 95(1) (2007) 138-162.
2
[3] Y. Tipsuwan, M.-Y. Chow, Control methodologies in networked control systems, Control engineering practice, 11(10) (2003) 1099-1111.
3
[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) 1746-1753.
4
[5] S. Kim, P. Park, C. Jeong, Robust H∞ stabilisation of networked control systems with packet analyser, IET control theory & applications, 4(9) (2010) 1828-1837.
5
[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) 511-518.
6
[7] P. Seiler, R. Sengupta, An H∞ approach to networked control, IEEE Transactions on Automatic Control, 50(3) (2005) 356-364.
7
[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) 638-650.
8
[9] H. Liu, Y. Shen, X. Zhao, Delay-dependent observer-based H∞ finite-time control for switched systems with time-varying delay, Nonlinear Analysis: Hybrid Systems, 6(3) (2012) 885-898.
9
[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) 1282-1297.
10
[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) 173-184.
11
[12] R. Wang, B. Wang, G.-P. Liu, W. Wang, D. Rees, H∞ controller design for networked predictive control systems based on the average dwell-time approach, IEEE Transactions on Circuits and Systems II: Express Briefs, 57(4) (2010) 310-314.
12
[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) 3565-3571.
13
[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.
14
[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) 349-365.
15
[16] P. Gahinet, A. Nemirovski, A. Laub, M. Chilali, LMI Control Toolbox User’s Guide. Natick, The MathWorks, Inc. P. Gahinet, (1995).
16
ORIGINAL_ARTICLE
Robust Adaptive Control of Voltage Saturated Flexible Joint Robots with Experimental Evaluations
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 un-modeled dynamics. Stability proof of the overall closed-loop 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 single-link flexible joint electrically driven robot, demonstrate a high performance of the proposed control schemes.
https://miscj.aut.ac.ir/article_1340_cd0e1ab4359401bfb1a2f885a223a9e4.pdf
2018-06-01
31
38
10.22060/miscj.2017.12174.5008
Robust Adaptive Control
real-time Implementation
Actuator saturation
Function approximation technique
A.
Izadbakhsh
izadbakhsh_alireza@hotmail.com
1
Department of Electrical Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran
LEAD_AUTHOR
[1] A. Izadbakhsh, A. Akbarzadeh Kalat, M.M. Fateh, S.M.R. Rafiei, A robust Anti-Windup control design for electrically driven robots-Theory and Experiment, International Journal of Control. Automation, and Systems, 9(5) (2011) 1005-1012.
1
[2] A. Izadbakhsh. Robust control design for rigid-link flexible-joint electrically driven robot subjected to constraint: theory and experimental verification, Nonlinear Dynamics, 85(2) (2016) 751-765.
2
[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) 147-156.
3
[4] W. Peng, Z. Lin, J. Su, Computed torque control-based composite nonlinear feedback controller for robot manipulators with bounded torques, IET Control Theory and Applications, 3(6) (2009) 701–711.
4
[5] A. Z-Rio, 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) 958-965.
5
[6] A. Z-Rio, 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) 386-391.
6
[7] E. Aguinaga-Ruiz, A. Zavala-Rio, 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) 934-944.
7
[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) 1333-1341.
8
[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) 544-555.
9
[10] A. Izadbakhsh, M. M. Fateh, Robust Lyapunov-based control of flexible-joint robots using voltage control strategy, Arabian journal for science and Engineering, 39(4) (2014) 3111-3121.
10
[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.
11
[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) 3-19.
12
[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.
13
[14] R. J. Caverly, D. E. Zlotnik, L. J. Bridgeman, J. R. Forbes, Saturated proportional derivative control of flexible-joint manipulators, Robotics and Computer- Integrated Manufacturing, 30(6) (2014) 658–666.
14
[15] R. J. Caverly, D. E. Zlotnik, J. R. Forbes, Saturated control of flexible-joint manipulators using a Hammerstein strictly positive real compensator, Robotica, 34(6) (2016) 1367-1382.
15
[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) 488-493.
16
[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) 53-62.
17
[18] A. Izadbakhsh, M. Masoumi, FAT-based robust adaptive control of flexible-joint robots: singular perturbation approach, IEEE Industrial Society’s 18th International Conference on Industrial Technology (ICIT), 2017, pp. 803-808.
18
[19] Z. Qu, D. M. Dawson, Robust tracking control of robot manipulators, IEEE Press, Inc., New York, 1996.
19
[20] K. S. Narendra, A. M. Annaswamy, Stable adaptive systems, Prentice Hall, Engle wood cliffs, NJ, 1989.
20
[21] W. Gao. R-R. Selmic, Adaptive Neural Network output feedback Control of Nonlinear Systems with Actuator Saturation, 44th IEEE Conference on Decision and Control, 2005, pp. 5522-5527.
21
[22] A. Izadbakhsh, M. M. Fateh, Real-time robust adaptive control of robots subjected to actuator voltage constraint, Nonlinear Dynamics, 78(3) (2014) 1999-2014.
22
[23] An-ch. Huang, M-Ch. Chen, Adaptive control of robot manipulators-A unified regressor free approach, World scientific, 2010.
23
[24] A. Izadbakhsh, Closed-form dynamic model of Puma560 robot arm, Proceedings of the 4th International Conf. on Autonomous Robots and Agents, 2009, pp. 675- 680.
24
[25] A. Izadbakhsh, A note on the nonlinear control of electrical flexible-joint robots, Nonlinear Dynamics, 89(4) (2017) 2753-2767.
25
ORIGINAL_ARTICLE
An Efficient Data Replication Strategy in Large-Scale Data Grid Environments Based on Availability and Popularity
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.
https://miscj.aut.ac.ir/article_2667_41886b2c3421bef3a7a35e93dff8e770.pdf
2018-06-01
39
50
10.22060/miscj.2017.12236.5020
Data Grid
Dynamic replication
File access pattern
Job Scheduling
N.
Mansouri
najme.mansouri@gmail.com
1
Computer Science Department, Shahid Bahonar University of Kerman, Kerman, Iran
LEAD_AUTHOR
M.M.
Javidi
javid@uk.ac.ir
2
Computer Science Department, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
[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) 187-200.
1
[2] N. Rathore, I. Chana, Variable threshold-based hierarchical load balancing technique in Grid, Engineering with Computers, 31(3) (2014) 597-615.
2
[3] A.S. Saleh, An efficient system-oriented grid scheduler based on a fuzzy matchmaking approach, Engineering with Computers, 29 (2013) 185-206.
3
[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.
4
[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) 236-246.
5
[6] S. Warhade, P. Dahiwale, M.M. Raghuwanshi, A dynamic data replication in grid system, in: 1st International Conference on Information Security & Privacy, 2016, 537-543.
6
[7] T. Hamrouni, S. Slimani, Faouzi Ben Charrada, A data mining correlated patterns-based periodic decentralized replication strategy for data grids, Journal of Systems and Software, 110 (2015) 10-27.
7
[8] E.U. Munir, J. Li, S. Shi, QoS suffrage heuristic for independent task scheduling in grid, Journal of Information Technology, 6 (2007)1166-1170.
8
[9] OptorSim–A Replica Optimizer Simulation: http://edg-wp2.web.cern.ch/edgwp2/ optimization/optorsim.html
9
[10] S. Goel, R. Buyya, Data replication strategies in wide-area distributed systems, Enterprise Service Computing: From Concept to Deployment, Idea Group Inc., Hershey, (2006) 211-241.
10
[11] Y. Saito, M. Shapiro, Optimistic replication, ACM Computing Surveys, 37(1) (2005) 42-81.
11
[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.
12
[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. 75-86.
13
[14] I. Foster, K. Ranganathan, Decoupling computation and data scheduling in distributed data-intensive applications, in: Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing, HPDC-11, IEEE, CS Press, Edinburgh, UK, 2002, pp. 352-358.
14
[15] M. Bsoul, A. Al-Khasawneh, E.E. Abdallah, Y. Kilani, Enhanced fast spread replication strategy for data grid, Journal of Network and Computer Applications, 34 (2011) 575-580.
15
[16] K. Sashi, A.S. Thanamani, Dynamic replica management for data grid, IACSIT International Journal of Engineering and Technology, 2 (2010) 329-333.
16
[17] R.S. Chang, H.P. Chang, A Dynamic data replication strategy using access-weight in data grids, The Journal of Supercomputing, 45 (2008) 277-295.
17
[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) 1324-1331.
18
[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.
19
[20] A. Horri, R. Sepahvand, G.H. Dastghaibyfard, A hierarchical scheduling and replication strategy, International Journal of Computer Science and Network Security, 8 (2008).
20
[21] N. Mansouri, G.H. Dastghaibyfard, Job scheduling and dynamic data replication in data grid environment, The Journal of Supercomputing, 64 (2013) 204-225.
21
[22] R. Chang, J. Chang, S. Lin, Job scheduling and data replication on data grids, Future Generation Computer Systems, 23 (2007) 846-860.
22
[23] N. Mansouri, G.H. Dastghaibyfard, A dynamic replica management strategy in data grid, Journal of Network and Computer Applications, 35 (2012) 1297-1303.
23
[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) 711-722.
24
[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) 534-543.
25
[26] C. Wang, C. Hsu, P. Liu, H. Chen, J. Wu, Optimizing server placement in hierarchical grid environments, The Journal of Supercomputing, 42 (2007) 267-282.
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[27] C. Yang, C. Fu, C. Hsu, File replication, maintenance, and consistency management services in data grids, The Journal of Supercomputing, 53 (2010) 411-439.
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[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. 106-109.
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[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. 257-262.
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[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) 1-14.
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[39] H. Hamad, E. AL-Mistarihi, C. Huah Yong, Response time optimization for replica selection service in data grids, Journal of Computer Science, 4 (2008) 487-493.
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[40] D.G. Cameron, R. Carvajal-schiaffino, A. Paul Millar, C. Nicholson, K. Stockinger, F. Zini, UK Grid Simulation with OptorSim, UK e-Science All Hands Meeting, (2003). 49.
40
ORIGINAL_ARTICLE
Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks
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, Duffing-Holmes chaotic systems (DHC) are used.
https://miscj.aut.ac.ir/article_2668_0c68c2223a3e4fa61ee92c30448f4236.pdf
2018-06-01
51
60
10.22060/miscj.2017.12805.5043
Dynamic Sliding Mode Control
Neural Model
Nonlinear system
Duffing-Holmes Chaotic System
A.
Karami-Mollaee
a_k_mollaee@yahoo.com
1
Faculty of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran
LEAD_AUTHOR
H.
Shanechi
shanechi@iit.edu
2
Faculty of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran.
AUTHOR
[1] J.-J. E. Slotine, W. Li, Applied nonlinear control, Prentice-Hall, 1991.
1
[2] H. Lee, V.-I. Utkin, Chattering suppression methods in sliding mode control systems, Elsevier, Annual Review in Control, 31 (2007) 179-188.
2
[3] A. Karami-Mollaee, 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) 141-150.
3
[4] W. Perruquetti, J. Pierre-Barbot, Sliding mode control in engineering, Marcel Dekker, 2002.
4
[5] T. Sun, H. Pei, Y. Pan, H. Zhou, C. Zhang, Neural network-based sliding mode adaptive control for robot manipulators, Elsevier, Neurocomputing, 74(14-15) (2011) 2377-2384.
5
[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) 56-62.
6
[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) 286-294.
7
[8] S. Mefoued, A second order sliding mode control and a neural network to drive a knee joint actuated orthosis, Elsevier, Neurocomputing, 155 (2015) 71-79.
8
[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.
9
[10] A. Levant, Sliding order and sliding accuracy in sliding mode control, International Journal of Control, 58 (1993) 1247-1263.
10
[11] G. Bartolini, A. Ferrara, E. Usai, Chattering avoidance by second-order sliding mode control, IEEE Transaction on Automatic Control, 43(2) (1998) 241-246.
11
[12] A. Levant, Robust exact differentiation via sliding mode techniques, Elsevier, Automatica, 34 (1998) 379-384.
12
[13] M. Norgaard, O. Ravn, N. K. Poulsen, L. K. Hansen, Neural network for modeling and control of dynamic systems, Springer, New York, 2001.
13
[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) 795-824.
14
[15] O. Kaynak, K. Erbatur, R. Ertugrul, The fusion of computationally Intelligent methodologies and sliding-mode control- a survey, IEEE Transaction on Industrial Electronic, 48(1) (2001) 4-17.
15
[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) 987-1002.
16
[17] M. K. Sifakis, S. J. Elliott, Adaptive tracking control of Duffing-Holmes chaotic systems with uncertainty, The 5th International Conference on Computer Science & Education, Hefei, China, August 24–27, 2010, pp. 1193- 1197.
17
ORIGINAL_ARTICLE
Kinematic and Dynamic Analyses of Tripteron, an Over-Constrained 3-DOF Translational Parallel Manipulator, through Newton-Euler Approach
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 over-constrained three translational degree-of-freedom parallel manipulator. Due to inconsistency between the number of equations and the unknowns, the problem of obtaining the constraint forces and torques of over-constraint 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 end-effector 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 end-effector 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.
https://miscj.aut.ac.ir/article_2828_152d692d32567d500d6cdad9bd36fad0.pdf
2018-06-01
61
70
10.22060/miscj.2018.13020.5055
Decoupled parallel manipulator
Dynamic analysis
Kinematic analysis
Over-constraint manipulator
Newton–Euler approach
A.
Arian
aarian@ut.ac.ir
1
Human and Robot Interaction Laboratory, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
AUTHOR
B.
Danaei
behzad.danaei@gmail.com
2
Human and Robot Interaction Laboratory, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
AUTHOR
M.
Tale Masouleh
m.t.masouleh@ut.ac.ir
3
Human and Robot Interaction Laboratory, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
LEAD_AUTHOR
[1] J.-P. Merlet, C. Gosselin, Parallel mechanisms and robots, in: Springer Handbook of Robotics, Springer, 2008, pp. 269-285.
1
[2] L.-W. Tsai, Robot analysis: the mechanics of serial and parallel manipulators, John Wiley & Sons, 1999
2
[3] M. Isaksson, T. Brogårdh, S. Nahavandi, Parallel manipulators with a rotation-symmetric arm system, Journal of mechanical design, 134(11) (2012) 114503.
3
[4] M. Isaksson, T. Brogårdh, M. Watson, S. Nahavandi, P. Crothers, The Octahedral Hexarot—A novel 6-DOF parallel manipulator, Mechanism and machine theory, 55 (2012) 91-102
4
[5] M. Isaksson, A. Eriksson, M. Watson, T. Brogårdh, S. Nahavandi, A method for extending planar axis-symmetric parallel manipulators to spatial mechanisms, Mechanism and Machine Theory, 83 (2015) 1-13
5
[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) 1-12
6
[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. 249-256
7
[8] M.T. Masouleh, M.H. Saadatzi, C.m. Gosselin, H.D. Taghirad, A geometric constructive approach for the workspace analysis of symmetrical 5-PRUR parallel mechanisms (3T2R), in: ASME Design Engineering Technical Conferences, (2010), pp. 15-18
8
[9] C. Quennouelle, C. Gosselin, Kinematostatic modeling of compliant parallel mechanisms, Meccanica, 46(1) (2011) 155-169
9
[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. 555-560
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[11] X. Kong, C.M. Gosselin, Kinematics and singularity analysis of a novel type of 3-CRR 3-DOF translational parallel manipulator, The International Journal of Robotics Research, 21(9) (2002) 791-798
11
[12] M. Sharifzadeh, M.T. Masouleh, A. Kalhor, On human–robot interaction of a 3-DOF decoupled parallel mechanism based on the design and construction of a novel and low-cost 3-DOF force sensor, Meccanica, 52(2017) 2471-2490
12
[13] R. Di Gregorio, V. Parenti-Castelli, 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. 269-277
13
[14] Y. Li, Q. Xu, Dynamic modeling and robust control of a 3-PRC translational parallel kinematic machine, Robotics and Computer-Integrated Manufacturing, 25(3) (2009) 630-640
14
[15] B. Danaei, A. Arian, M.T. Masouleh, A. Kalhor, Dynamic modeling and base inertial parameters determination of a 2-DOF spherical parallel mechanism, Multibody System Dynamics, 41(4) (2017) 367-390
15
[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) 2393-2418
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[18] A. Arian, B. Danaei, M.T. Masouleh, Kinematics and dynamics analysis of a 2-DOF spherical parallel robot, in: Robotics and Mechatronics (ICROM), 2016 4th International Conference on, IEEE, 2016, pp. 154-159
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[19] Z. Bi, S. Lang, M. Verner, Dynamic modeling and validation of a tripod-based machine tool, The International Journal of Advanced Manufacturing Technology, 37(3-4) (2008) 410-421
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[20] Y.-W. Li, J.-S. Wang, L.-P. Wang, X.-J. Liu, Inverse dynamics and simulation of a 3-DOF spatial parallel manipulator, in: Robotics and Automation, 2003. Proceedings. ICRA’03. IEEE International Conference on, IEEE, 2003, pp. 4092-4097
20
[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. 1-6
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[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. 1153-1157
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[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) 3389-3401
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29
ORIGINAL_ARTICLE
Forecasting Gold Price Changes: Application of an Equipped Artificial Neural Network
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 meta-heuristic 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 Neuro-Fuzzy 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 BAT-Neural Network (BNN) outperforms both conventional and modern forecasting models.
https://miscj.aut.ac.ir/article_2827_9dfae0734b49615b4ab52ed19ca28d6f.pdf
2018-06-01
71
82
10.22060/miscj.2018.13508.5074
Artificial Intelligence
BAT Algorithm
Forecasting
Gold Price Fluctuations
Neural Network
R.
Hafezi
r.hafezi@aut.ac.ir
1
Technology Foresight Group, Department of Management, Science and Technology, Amirkabir University of Technology, Tehran, Iran.
AUTHOR
A.
Akhavan
akhavan@aut.ac.ir
2
Technology Foresight Group, Department of Management, Science and Technology, Amirkabir University of Technology, Tehran, Iran.
LEAD_AUTHOR
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4
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ORIGINAL_ARTICLE
Presenting a Model for Multiple-Step-Ahead-Forecasting of Volatility and Conditional Value at Risk in Fossil Energy Markets
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 multiple-step-ahead 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 multiple-step-ahead forecast functions as a verb.
https://miscj.aut.ac.ir/article_2834_3f31885a3465d673dc3ff5c43219c48d.pdf
2018-06-01
83
94
10.22060/miscj.2018.13473.5073
Multiple-step-ahead Forecasting
Volatility
Value at Risk
Conditional Value at Risk
ES models
E.
Mohammadian Amiri
emohammadian@email.kntu.ac.ir
1
Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
AUTHOR
S. B.
Ebrahimi
b_ebrahimi@kntu.ac.ir
2
Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
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52
ORIGINAL_ARTICLE
Adaptive attitude controller of a reentry vehicles based on Back-stepping Dynamic inversion method
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 Backstepping-Dynamic inversion-Adaptive (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 closed-loop 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 (6-DOF) model of the re-entry capsule. Also, the results of the proposed control algorithm will be compared with the Backstepping Dynamic inversion (B.D) control method.
https://miscj.aut.ac.ir/article_2669_f12d0b3e168755a10e53fcd1bbc6bb13.pdf
2018-06-01
95
106
10.22060/miscj.2017.12907.5048
Attitude control
Backstepping-Dynamic inversion-Adaptive
Reusable Launch Vehicle (RLV)
Controller output constraint
Thruster model
A.
Mohseni
abdollah.mohseni@aut.ac.ir
1
Department of Aerospace Engineering, Amirkabir University of Technology, 15875-4413, Tehran, Iran.
AUTHOR
F.
Fani Saberi
f.sabery@aut.ac.ir
2
Space Science and Technology Institute, Amirkabir University of Technology, 15875-4413, Tehran, Iran.
LEAD_AUTHOR
M.
Mortazavi
mortazavi@aut.ac.ir
3
Department of Aerospace Engineering, Amirkabir University of Technology, 15875-4413, Tehran, Iran.
AUTHOR
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