Soft computing-based active vibration control of a flexible structure

Control of vibration of flexible structures has been of remarkable research attention in the last decade. Conventional control methods have not been widely successful due to the dynamic complexity of flexible structures. The literature has recently seen an emergence of demand of soft computing techniques in modelling and control of such dynamic systems. However, the form of soft computing required depends on the nature of the application. This paper accordingly presents investigations into modelling and control techniques based on soft computing methods for vibration suppression of two-dimensional flexible plate structures. The design and analysis of an active vibration control (AVC) system utilising soft computing techniques including neural networks and fuzzy logic is presented. The investigation involves soft computing approach with single-input single-output (SISO) and single-input multi-output (SIMO) AVC structures. A comprehensive comparative assessment of the approaches in terms of performance and design efficiency is also provided. Investigations reveal that the developed soft computing-based AVC system performs very well in the suppression of vibration of a flexible plate structure. It is also shown that the developed SIMO AVC system performs much better in the suppression of vibration of a flexible plate structure in comparison to the SISO AVC system.

[1]  J. Van De Vegte,et al.  Design of passive vibration controls for internally damped beams by modal control techniques , 1976 .

[2]  Susan I. Hruska,et al.  Back-propagation learning in expert networks , 1992, IEEE Trans. Neural Networks.

[3]  M. O. Tokhi,et al.  Active Noise Control Using Multi-Layered Perceptron Neural Networks , 1997 .

[4]  Shuzhi Sam Ge,et al.  Adaptive neural network control of flexible link robots based on singular perturbation , 1997, Proceedings of the 1997 IEEE International Conference on Control Applications.

[5]  Robert J. Bernhard,et al.  ADAPTIVE PASSIVE VIBRATION CONTROL , 1996 .

[6]  C. H. Choi,et al.  Active vibration control of a flexible beam, based on flow source control , 1999 .

[7]  Tiam Lin Sze System identification using radial basis function networks , 1995 .

[8]  Martin Casdagli,et al.  Nonlinear prediction of chaotic time series , 1989 .

[9]  P. E. Allaire,et al.  Active Vibration Control of a Single Mass Rotor on Flexible Supports , 1983 .

[10]  Keith E. Rouch,et al.  Optimal passive vibration control of cutting process stability in milling , 1991 .

[11]  Heidar Ali Talebi,et al.  Inverse dynamics control of flexible-link manipulators using neural networks , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[12]  R. Stanway,et al.  Active vibration control of turbomachinery: A numerical investigation of modal controllers , 1988 .

[13]  Chak Chantalakhana Model-based control of plate vibrations using active constrained layer damping , 2000 .

[14]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[15]  Andy J. Keane,et al.  Passive vibration control via unusual geometries: experiments on model aerospace structures , 1996 .

[16]  Uwe Stöbener,et al.  Active Vibration Control of a Car Body Based on Experimentally Evaluated Modal Parameters , 2001 .

[17]  M. Osman Tokhi,et al.  Dynamic modelling of a single-link flexible manipulator: parametric and non-parametric approaches , 2002, Robotica.

[18]  Abhijit Mukherjee,et al.  Active vibration control of piezolaminated stiffened plates , 2002 .

[19]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[20]  J.-S.R. Jang,et al.  Input selection for ANFIS learning , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[21]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[22]  Hua Jun Li,et al.  H2 active vibration control for offshore platform subjected to wave loading , 2003 .

[23]  S Z Qin,et al.  Comparison of four neural net learning methods for dynamic system identification , 1992, IEEE Trans. Neural Networks.

[24]  U. R. Prasad,et al.  Back propagation through adjoints for the identification of nonlinear dynamic systems using recurrent neural models , 1994, IEEE Trans. Neural Networks.

[25]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[26]  R. Hecht-Nielsen Counterpropagation networks. , 1987, Applied optics.

[27]  Marian B. Gorzalczany,et al.  Neuro-fuzzy networks in time series modelling , 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

[28]  F. A. Johnson,et al.  Active vibration control for marine applications , 2004 .

[29]  Stephen A. Billings,et al.  Self-tuning and adaptive control: theory and applications , 1981 .

[30]  Alan S. Lapedes,et al.  Successive approximation radial basis function networks for nonlinear modeling and prediction , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[31]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[32]  Eiji Mizutani,et al.  Coactive neural fuzzy modeling , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[33]  M. O. Tokhi,et al.  Active noise control systems , 1987 .

[34]  M. O. Tokhi,et al.  Design and implementation of self-tuning active noise control systems , 1991 .

[35]  Kunihiko Fukushima,et al.  Cognitron: A self-organizing multilayered neural network , 1975, Biological Cybernetics.

[36]  J. Orbach Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. , 1962 .

[37]  Shinji Yamada,et al.  Active noise control , 1987 .

[38]  Ronald J. Williams,et al.  Experimental Analysis of the Real-time Recurrent Learning Algorithm , 1989 .

[39]  Jaehwan Kim,et al.  Interaction of active and passive vibration control of laminated composite beams with piezoceramic sensors/actuators , 2002 .

[40]  Stephen Grossberg,et al.  Competitive Learning: From Interactive Activation to Adaptive Resonance , 1987, Cogn. Sci..

[41]  C. W. de Silva,et al.  An algorithm for the optimal design of passive vibration controllers for flexible systems , 1981 .

[42]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[43]  J. Holterman,et al.  Active structural elements within a general vibration control framework , 2000 .

[44]  A. Lapedes,et al.  Nonlinear modeling and prediction by successive approximation using radial basis functions , 1994 .