پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 399 |
| تعداد مقالات | 5,389 |
| تعداد مشاهده مقاله | 5,288,021 |
| تعداد دریافت فایل اصل مقاله | 4,882,768 |
Behavioral Modeling and Experimental Verification of a Smart Servomotor Used in a Thermal Control Louver of a Satellite Using Dynamic Neural Network | ||
| AUT Journal of Mechanical Engineering | ||
| مقاله 1، دوره 5، شماره 1، خرداد 2021، صفحه 3-12 اصل مقاله (925.18 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajme.2020.16517.5822 | ||
| نویسندگان | ||
| Saeid Shakiba* 1؛ mohsen abedi2؛ Amirhosein Vedadi3 | ||
| 1School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| 2Satellite Research Institute, Iranian Space Research Center, Tehran, Iran | ||
| 3Bachelor student, School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| Louvers are powerful devices for the thermal management of satellites. Nevertheless, the high mass and power consumption and the low reliability of servomotors serving as the actuators of louvers, make the space applications of these technologies very restricted. To tackle this problem, this paper utilizes a shape memory alloy to build a smart servomotor for use in a laboratory louver. The major bottleneck of the use of thermal shape memory alloys is the existence of complex nonlinear hysteretic characteristics in the behavior of these materials. In this paper, a nonlinear autoregressive exogenous model is proposed to predict the nonlinear hysteric behavior of a shape memory alloy. This model is based on a dynamic neural network that its fine function is achieved by a suitable selection of the architecture and the transfer functions of the output and hidden layers. The proposed model is first trained with a batch of test data at the frequency of 0.01 Hz and then validated with another batch of data at the frequency of 0.008 Hz. The training and validation data are obtained from a laboratory louver equipped with a spring of shape memory alloy as the opening actuator of blades. The mean square error of the proposed model for the training and validation data is 1.0325 and 1.0835 degrees, respectively. | ||
| کلیدواژه‌ها | ||
| Hysteresis؛ NARX model؛ Dynamic neural network؛ Shape memory alloy؛ Thermal control louver | ||
| مراجع | ||
|
[1] J.M. Jani, M. Leary, A. Subic, M.A. Gibson, A review of shape memory alloy research, applications and opportunities, Materials & Design (1980-2015), 56 (2014) 1078-1113. [2] S. Shakiba, M.R. Zakerzadeh, M. Ayati, Experimental characterization and control of a magnetic shape memory alloy actuator using the modified generalized rate-dependent Prandtl–Ishlinskii hysteresis model, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, (2018) 0959651818758910. [3] S.Shakiba, A. Yousefi Koma, M. Jokar, M.R. Zakerzadeh, H. Basaeri, Modeling and characterization of the shape memory alloy–based morphing wing behavior using proposed rate-dependent Prandtl-Ishlinskii models, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, (2019) 1-16. [4] A. Nassiri-monfared, M. Baghani, M.R. Zakerzadeh, P. Fahimi, Developing a semi-analytical model for thermomechanical response of SMA laminated beams, considering SMA asymmetric behavior, Meccanica, 53(4) (2018) 957-971. [5] F. Mirzakhani, S. Ayati, P. Fahimi, M. Baghani, Online force control of a shape-memory-alloy-based 2 degree-of-freedom human finger via inverse model and proportional–integral–derivative compensator, Journal of Intelligent Material Systems and Structures, 30(10) (2019) 1538-1548. [6] J.-Y. Gauthier, C. Lexcellent, A. Hubert, J. Abadie, N. Chaillet, Modeling rearrangement process of martensite platelets in a magnetic shape memory alloy Ni2MnGa single crystal under magnetic field and (or) stress action, Journal of intelligent material systems and structures, 18(3) (2007) 289-299. [7] S. Shakki, M.R. Zakerzadeh, Modeling and control of a shape memory alloy actuator using fuzzy sliding mode controller, Modares Mechanical Engineering, 16(7) (2016(in Persian)) 353-360. [8] D. Hughes, J.T. Wen, Preisach modeling of piezoceramic and shape memory alloy hysteresis, Smart materials and structures, 6(3) (1997) 287. [9] G. Song, V. Chaudhry, C. Batur, A neural network inverse model for a shape memory alloy wire actuator, Journal of intelligent material systems and structures, 14(6) (2003) 371-377. [10] G. Song, V. Chaudhry, C. Batur, Precision tracking control of shape memory alloy actuators using neural networks and a sliding-mode based robust controller, Smart Materials and Structures, 12(2) (2003) 223. [11] J. Ko, M.B. Jun, G. Gilardi, E. Haslam, E.J. Park, Fuzzy PWM-PID control of cocontracting antagonistic shape memory alloy muscle pairs in an artificial finger, Mechatronics, 21(7) (2011) 1190-1202. [12] S. Shakki, M.R. Zakerzadeh, M. Ayati, O. Jeddinia, Modeling and experimental verification of a magnetic shape memory alloy actuator behavior using modified generalized rate-dependent Prandtl-Ishlinskii model, Modares Mechanical Engineering, 16(11) (2017(in persian)) 389-396. [13] B. Minorowicz, G. Leonetti, F. Stefanski, G. Binetti, D. Naso, Design, modelling and control of a micro-positioning actuator based on magnetic shape memory alloys, Smart Materials and Structures, 25(7) (2016) 075005. [14] H. Mai, G. Song, X. Liao, Time-delayed dynamic neural network-based model for hysteresis behavior of shape-memory alloys, Neural Computing and Applications, 27(6) (2016) 1519-1531. [15] H. Wang, G. Song, Innovative NARX recurrent neural network model for ultra-thin shape memory alloy wire, Neurocomputing, 134 (2014) 289-295. [16] M. Lallart, K. Li, Z. Yang, W. Wang, System-level modeling of nonlinear hysteretic piezoelectric actuators in quasi-static operations, Mechanical Systems and Signal Processing, 116 (2019) 985-996. [17] S. Yi, B. Yang, G. Meng, Ill-conditioned dynamic hysteresis compensation for a low-frequency magnetostrictive vibration shaker, Nonlinear Dynamics, 96(1) (2019) 535-551. [18] H. Liu, S. Pu, J. Cao, X. Yang, Z. Wang, Torque Ripple Mitigation of T-3L Inverter Fed Open-End Doubly-Salient Permanent-Magnet Motor Drives Using Current Hysteresis Control, Energies, 12(16) (2019) 3109. [19] S. Çoruh, F. Geyikçi, E. Kılıç, U. Çoruh, The use of NARX neural network for modeling of adsorption of zinc ions using activated almond shell as a potential biosorbent, Bioresource technology, 151 (2014) 406-410. [20] S. Shakki, A. Yousefi-Koma, M. Jokar, M.R. Zakerzadeh, H. Basaeri, Hysteresis Modeling of Shape Memory Alloy Actuators using Generalized Rate-Dependent Prandtl-Ishlinskii Model, in: Iranian Society of Acoustics and Vibration(ISAV), Iran, Tehran, 2016.
| ||
|
آمار تعداد مشاهده مقاله: 413 تعداد دریافت فایل اصل مقاله: 358 |
||