Document Type : Research Articles
Authors
1 Faculty of Electrical Engineering and Computer, University of Birjand, Iran.
2 Electrical Engineering department, Arak University, Arak, Iran Iran.
Abstract
An earthquake is a sudden and destructive natural disaster that often results in unpredictable damage to human life and property. Investigating the effects of earthquakes on buildings and enhancing the seismic performance of structures is a crucial approach for mitigating severe damage during such events. One effective tool in testing the resistance of structures against earthquakes is the use of shaking tables. In this paper, the stabilization and control of earthquake simulator using a fuzzy sliding mode controller (FSMC) and adaptive unscented Kalman filter (AUKF)and adaptive extended Kalman filter (AEKF) is presented. These filters employ a recursive technique to effectively adjust the noise covariance by utilizing an adaptation method known as the steepest descent. In the proposed approach, the shaking table states are estimated using an accelerometer, encoder, and camera. These estimated states are then utilized by the AEKF/AUKF to stabilize and control the closed-loop system. A fuzzy sliding mode controller is designed to track the reference input, and eliminate external disturbances and noise. In the control of sliding mode, the occurrence of chattering in the control input is unavoidable. To mitigate this undesired chattering phenomenon, a fuzzy inference mechanism has been employed. The image processing approach has been utilized to measure the displacement online using the camera. The advantages of using the camera include not requiring direct contact with the table, as well as offering a low price and good accuracy.
Keywords
Main Subjects
[2] S. Chen, H. Zhuang, D. Quan, J. Yuan, K. Zhao, and B. Ruan, "Shaking table test on the seismic response of largescale subway station in a loess site: A case study," Soil Dynamics and Earthquake Engineering, vol. 123, pp. 173-184, 2019.
[3] H. Yang, D. Cong, Z. Yang, and J. Han, "A Practical Adaptive Sinusoidal Vibration Control Strategy for Electro-Hydraulic Shake Table," Journal of Vibration Engineering & Technologies, vol. 11, pp. 1725–1739, 2023
[4] S. Pampanin et al., "Triaxial shake table testing of an integrated low‐damage building system," Earthquake Engineering & Structural Dynamics, 2023.
[5] M. Flah, M. Ragab, M. Lazhari, and M. Nehdi, "Localization and classification of structural damage using deep learning single-channel signal-based measurement," Automation in Construction, vol. 139, p. 104271, 2022.
[6] M. Diaz, P.-É. Charbonnel, and L. Chamoin, "A new Kalman filter approach for structural parameter tracking: application to the monitoring of damaging structures tested on shaking-tables," Mechanical Systems and Signal Processing, vol. 182, p. 109529, 2023.
[7] M. A. Kuddus, J. Li, H. Hao, C. Li, and K. Bi, "Targetfree vision-based technique for vibration measurements of structures subjected to out-of-plane movements," Engineering Structures, vol. 190, pp. 210-222, 2019.
[8] J. Wen, C. Zhao, and Z. Shi, "LSTM‐based adaptive robust nonlinear controller design of a single‐axis hydraulic shaking table," IET Control Theory & Applications, vol. 17, no. 7, pp. 825-836, 2023.
[9] A. Najafi and B. F. Spencer Jr, "Modified model‐based control of shake tables for online acceleration tracking," Earthquake Engineering & Structural Dynamics, vol. 49, no. 15, pp. 1721-1737, 2020.
[10] J. Yao et al., "Sinusoidal acceleration harmonic estimation using the extended Kalman filter for an electro-hydraulic servo shaking table," Journal of Vibration and Control, vol. 21, no. 8, pp. 1566-1579, 2015.
[11] J. Yao, D. Di, G. Jiang, S. Gao, and H. Yan, "Real-time acceleration harmonics estimation for an electrohydraulic servo shaking table using Kalman filter with a linear model," IEEE Transactions on Control Systems Technology, vol. 22, no. 2, pp. 794-800, 2013.
[12] Z. Lu, Z. Wang, Y. Zhou, and X. Lu, "Nonlinear dissipative devices in structural vibration control: A review," Journal of Sound and Vibration, vol. 423, pp. 18-49, 2018.
[13] J. Lu, H. Xie, L. Hu, H. Yang, and Y. Chen, "Variableparameter feedforward control for centrifuge shaking table based on nonlinear frequency characteristic model," Mechanical Systems and Signal Processing, vol. 161, p. 108011, 2021.
[14] Y. Tang, G. Shen, Z.-C. Zhu, X. Li, and C.-F. Yang, "Time waveform replication for electro-hydraulic shaking table incorporating off-line iterative learning control and modified internal model control," Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 228, no. 9, pp. 722-733, 2014, doi: 10.1177/0959651814536553.
[15] S. Welikala, H. Lin, and P. Antsaklis, "A Decentralized Analysis and Control Synthesis Approach for Networked Systems with Arbitrary Interconnections," arXiv preprint arXiv:2204.09756, 2022.
[16] F. Beltran-Carbajal and G. Silva-Navarro, "Output feedback dynamic control for trajectory tracking and vibration suppression," Applied Mathematical Modelling, vol. 79, pp. 793-808, 2020.
[17] G. Shen, G.-M. Lv, Z.-M. Ye, D.-C. Cong, and J.-W. Han, "Implementation of electrohydraulic shaking table controllers with a combined adaptive inverse control and minimal control synthesis algorithm," Iet control theory & applications, vol. 5, no. 13, pp. 1471-1483, 2011.
[18] T. B. Ma and F. Du, "Minimal control synthesis algorithm for panel vibration control," Advanced Materials Research, vol. 804, pp. 269-274, 2013.
[19] C.-h. Gao and X.-b. Yuan, "Development of the shaking table and array system technology in China," Advances in Civil Engineering, vol. 2019, 2019.
[20] O. A. Al-Fahdawi and L. R. Barroso, "Adaptive neurofuzzy and simple adaptive control methods for full threedimensional coupled buildings subjected to bi-directional seismic excitations," Engineering Structures, vol. 232, p. 111798, 2021.
[21] A. S. Sayed, A. T. Azar, Z. F. Ibrahim, H. A. Ibrahim, N. A. Mohamed, and H. H. Ammar, "Deep learning based kinematic modeling of 3-rrr parallel manipulator," in Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020), 2020: Springer, pp. 308-321.
[22] E. W. Suseno and A. Ma'arif, "Tuning of PID controller parameters with genetic algorithm method on DC motor," International Journal of Robotics and Control Systems, vol. 1, no. 1, pp. 41-53, 2021.
[23] H. Zhang, L. Wang, and W. Shi, "Seismic control of adaptive variable stiffness intelligent structures using fuzzy control strategy combined with LSTM," Journal of Building Engineering, vol. 78, p. 107549, 2023.
[24] K. Seki, M. Iwasaki, M. Kawafuku, H. Hirai, and K. Yasuda, "Adaptive compensation for reaction force with frequency variation in shaking table systems," IEEE Transactions on Industrial Electronics, vol. 56, no. 10, pp. 3864-3871, 2009.
[25] S. Strano and M. Terzo, "A non-linear robust control of a multi-purpose earthquake simulator," in Proceedings of the World Congress on Engineering, 2013, vol. 3.
[26] M. Soleymani, A. Khalatabari-S, and B. Ghanbari-S, "Fuzzy-sliding-mode supervisory control of a seismic shake table with variable payload for robust and precise acceleration tracking," Journal of earthquake Engineering, vol. 23, no. 4, pp. 539-556, 2019.
[27] M. Tárník and J. Murgaš, "Model reference adaptive control of permanent magnet synchronous motor," Journal of Electrical Engineering, vol. 62, no. 3, pp. 117-125, 2011.
[28] C. J. O’Rourke, M. M. Qasim, M. R. Overlin, and J. L. Kirtley, "A geometric interpretation of reference frames and transformations: dq0, clarke, and park," IEEE Transactions on Energy Conversion, vol. 34, no. 4, pp. 2070-2083, 2019.
[29] X. Li and G. Gong, "Objective-oriented genetic algorithm based dynamical sliding mode control for slurry level and air pressure in shield tunneling," Automation in Construction, vol. 109, p. 102987, 2020.
[30] H. Komurcugil, S. Biricik, S. Bayhan, and Z. Zhang, "Sliding mode control: Overview of its applications in power converters," IEEE Industrial Electronics Magazine, vol. 15, no. 1, pp. 40-49, 2020.
[31] X. Cheng, X. Liu, X. Li, and Q. Yu, "An intelligent fusion estimation method for state of charge estimation of lithium-ion batteries," Energy, vol. 286, p. 129462, 2024.
[32] N. Kayhani, W. Zhao, B. McCabe, and A. P. Schoellig, "Tag-based visual-inertial localization of unmanned aerial vehicles in indoor construction environments using an onmanifold extended Kalman filter," Automation in Construction, vol. 135, p. 104112, 2022.
[33] H. Tang et al., "Feature extraction of multi-sensors for early bearing fault diagnosis using deep learning based on minimum unscented kalman filter," Engineering Applications of Artificial Intelligence, vol. 127, p. 107138, 2024.
[34] I. Ullah, Y. Shen, X. Su, C. Esposito, and C. Choi, "A localization based on unscented Kalman filter and particle filter localization algorithms," IEEE Access, vol. 8, pp. 2233-2246, 2019.
[35] Y. Zhang, M. Li, Y. Zhang, Z. Hu, Q. Sun, and B. Lu, "An enhanced adaptive unscented kalman filter for vehicle state estimation," IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-12, 2022.
[36] D. Lee, G. Vukovich, and R. Lee, "Robust unscented Kalman filter for nanosat attitude estimation," International Journal of Control, Automation and Systems, vol. 15, pp. 2161-2173, 2017.
[37] J. H. Yoon, D. Y. Kim, and V. Shin, "Window length selection in linear receding horizon filtering," in 2008 International Conference on Control, Automation and Systems, 2008: IEEE, pp. 2463-2467.
[38] R.Havangi,F.Karimi, "Improvement of The Battery State of Charge Estimation Using Recursive Least Square Based Adaptive Extended Kalman Filter "International Journal of Industrial Electronics, Control and Optimization ,vol.7,no.2,pp.141-151,2024