Modelling and control of horizontal flexible plate using particle swarm optimization

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    This paper presents the modeling and active vibration control using an evolutionary swarm algorithm known as particle swarm optimization. Initially, a flexible plate experimental rig was designed and fabricated with all clamped edges as boundary conditions constrained at horizontal position. The purpose of the experimental rig development is to collect the input-output vibration data. Next, the data acquisition and instrumentation system were designed and integrated with the experimental rig. Several procedures were conducted to acquire the input-output vibration data. The collected vibration data were then utilized to develop the system model. The parametric modeling using particle swarm optimization was devised using an auto regressive model with exogenous model structure. The developed model was validated using mean square error, one step ahead prediction, correlation tests and pole-zero diagram stability. Then, the developed model was used for the development of controller using an active vibration control technique. It was found that particle swarm optimization based on the active vibration control using Ziegler-Nichols method has successfully suppressed the unwanted vibration of the horizontal flexible plate system. The developed controller achieved the highest attenuation value at the first mode of vibration which is the dominant mode in the system with 34.37 dB attenuation.


  • Keywords

    Flexible plate; Active Vibration Control; Particle Swarm Optimization; System Identification.

  • References

      [1] Mat Darus IZ, Al-Khafaji AAM. Non-parametric modelling of a rectangular flexible plate structure. Engineering Applications of Artificial Intelligence. 2012;25(1):94-106.

      [2] Julai S, Tokhi MO, Mohamad M, Latiff IA, editors. Control of a flexible plate structure using particle swarm optimization. 2009 IEEE Congress on Evolutionary Computation; 2009: IEEE.

      [3] Lueg P. Process of silencing sound oscillations. Google Patents; 1936.

      [4] Tokhi MO, Hossain MA. Self-tuning active vibration control in flexible beam structures. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 1994;208(4):263-78.

      [5] Mat Darus IZ, Tokhi MO. Soft computing-based active vibration control of a flexible structure. Engineering Applications of Artificial Intelligence. 2005;18(1):93-114.

      [6] Hashim MSZ, Tokhi M, Mat Darus I. Active vibration control of flexible structures using genetic optimisation. Journal of low frequency noise, vibration and active control. 2006;25(3):195-207.

      [7] Madkour A, Hossain MA, Dahal KP, Yu H. Intelligent learning algorithms for active vibration control. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 2007;37(5):1022-33.

      [8] Marinaki M, Marinakis Y, Stavroulakis GE. Fuzzy control optimized by PSO for vibration suppression of beams. Control Engineering Practice. 2010;18(6):618-29.

      [9] Julai S, Tokhi MO, editors. SISO and SIMO active vibration control of a flexible plate structure using real-coded genetic algorithm. Cybernetic Intelligent Systems (CIS), 2010 IEEE 9th International Conference on; 2010: IEEE.

      [10] Caruso G, Galeani S, Menini L. Active vibration control of an elastic plate using multiple piezoelectric sensors and actuators. Simulation Modelling Practice and Theory. 2003;11(5):403-19.

      [11] Gardonio P, Bianchi E, Elliott S. Smart panel with multiple decentralized units for the control of sound transmission. Part I: theoretical predictions. Journal of sound and vibration. 2004;274(1):163-92.

      [12] Gardonio P, Bianchi E, Elliott S. Smart panel with multiple decentralized units for the control of sound transmission. Part II: design of the decentralized control units. Journal of Sound and Vibration. 2004;274(1):193-213.

      [13] Gardonio P, Bianchi E, Elliott S. Smart panel with multiple decentralized units for the control of sound transmission. Part III: control system implementation. Journal of sound and vibration. 2004;274(1):215-32.

      [14] Hu Q, Ma G, Li C, editors. Active vibration control of a flexible plate structure using LMI-based H∞ output feedback control law. Intelligent Control and Automation, 2004 WCICA 2004 Fifth World Congress on; 2004: IEEE.

      [15] Tokhi MO, Hossain MA. A unified adaptive active control mechanism for noise cancellation and vibration suppression. Mechanical systems and signal processing. 1996;10(6):667-82.

      [16] Tavakolpour AR, Mat Darus IZ, Tokhi MO, Mailah M. Genetic algorithm-based identification of transfer function parameters for a rectangular flexible plate system. Engineering Applications of Artificial Intelligence. 2010;23(8):1388-97.

      [17] Mat Darus IZ. Soft computing adaptive Vibration control of flexible structures: Ph. D. Thesis, Department of Automatic control and System Engineering, University of Sheffield; 2004.

      [18] Shaheed MH, Tokhi MO, Chipperfield AJ, Azad AKM. Modelling and open-loop control of a single-link flexible manipulator with genetic algorithms. Journal of Low Frequency Noise, Vibration and Active Control. 2001;20(1):39-55.

      [19] Al-Khafaji AAM. Modeling and control of underwater flexible-link manipulator employing bio-inspired algorithms and PID-based control schemes: Universiti Teknologi Malaysia; 2016.

      [20] Md Salleh S, Tokhi MO, Julai S, Mohamad M, Latiff IA, editors. PSO-based parametric modelling of a thin plate structure. Computer Modeling and Simulation, 2009 EMS'09 Third UKSim European Symposium on; 2009: IEEE.

      [21] Noghrehabadi A, Ghalambaz M, Ghalambaz M, Vosough A. A hybrid power series—Cuckoo search optimization algorithm to electrostatic deflection of micro fixed-fixed actuators. International Journal of Multidisciplinary Sciences and Engineering. 2011;2(4):22-6.

      [22] Hadi MS, Mat Darus IZ, Yatim H, editors. Modeling flexible plate structure system with free-free-clamped-clamped (FFCC) edges using Particle Swarm Optimization. Computers & Informatics (ISCI), 2013 IEEE Symposium on; 2013: IEEE.

      [23] Abd Samad MF, Jamaluddin H, Ahmad R, Yaacod MS, Azad AKM. Effect of Penalty Function Parameter in Objective Function of System Identification. International Journal of Automotive and Mechanical Engineering (IJAME). 2013:940-54.

      [24] Abdel-Raouf O, Abdel-Baset M. A new hybrid flower pollination algorithm for solving constrained global optimization problems. International Journal of Applied Operational Research-An Open Access Journal. 2014;4(2):1-13.

      [25] Azraai MR, Priyandoko G, Yusoff AR, Rashid MFFA. Parametric optimization of magneto-rheological fluid damper using particle swarm optimization. International Journal of Automotive and Mechanical Engineering. 2015;11:2591.

      [26] Mohd Yatim H. Evolutionary algorithms for active vibration control of flexible manipulator: Universiti Teknologi Malaysia; 2016.

      [27] Hadi MS, Mat Darus IZ. Intelligence swarm model optimization of flexible plate structure system. International Review of Automatic Control (IREACO). 2013;6(3):322-31.

      [28] Leitch RR, Tokhi MO. Active noise control systems. IEE Proceedings A-Physical Science, Measurement and Instrumentation, Management and Education-Reviews. 1987;134(6):525-46.

      [29] Blevins TL. PID advances in industrial control. IFAC Proceedings Volumes. 2012;45(3):23-8.

      [30] Saad MS. Evolutionary optimization and real-time self-tuning active vibration control of a flexible beam system: Universiti Teknologi Malaysia; 2014.

      [31] Shaharuddin NMR. Active vibration control of transverse vibrating segmented marine riser: Universiti Teknologi Malaysia; 2015.




Article ID: 13117
DOI: 10.14419/ijet.v7i2.29.13117

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.