Genetic algorithm-based identification of transfer function parameters for a rectangular flexible plate system

This paper focuses on an identification technique based on genetic algorithms (GAs) with application to rectangular flexible plate systems for active vibration control. A real coded GA with a new truncation-based selection strategy of individuals is developed, to allow fast convergence to the global optimum. A simulation environment characterizing the dynamic behavior of a flexible rectangular plate system is developed using the central finite difference (FD) techniques. The plate thus developed is excited by a uniformly distributed random disturbance and the input-output data of the system acquired is used for black-box modeling the system with the GA optimization using an autoregressive model structure. Model validity tests based on statistical measures and output prediction are carried out. The prediction capability of the model is further examined with unseen data. It is demonstrated that the GA gives faster convergence to an optimum solution and the model obtained characterizes the dynamic system behavior of the system well.

[1]  W.A. Bedwani,et al.  Genetic optimization of variable structure PID control systems , 2001, Proceedings ACS/IEEE International Conference on Computer Systems and Applications.

[2]  D. Puangdownreong,et al.  Model Identification of Cart-plus-Pendulum System Using Genetic Algorithm , 2006, TENCON 2006 - 2006 IEEE Region 10 Conference.

[3]  S. Timoshenko,et al.  THEORY OF PLATES AND SHELLS , 1959 .

[4]  D. E. Goldberg,et al.  Optimization and Machine Learning , 2022 .

[5]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[6]  David Coley,et al.  Introduction to Genetic Algorithms for Scientists and Engineers , 1999 .

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  Jun Zhang,et al.  Comparison of Performance between Different Selection Strategies on Simple Genetic Algorithms , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[9]  Guy Albert Dumont,et al.  System identification and control using genetic algorithms , 1992, IEEE Trans. Syst. Man Cybern..

[10]  William S. Levine,et al.  The Control Handbook , 2005 .

[11]  Intan Zaurah Mat Darus,et al.  Performance evaluation of finite difference and finite element methods applied to flexible thin plate for active vibration control , 2008 .

[12]  Ataollah Ebrahimzadeh,et al.  Intelligent digital signal-type identification , 2008, Eng. Appl. Artif. Intell..

[13]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[14]  Carlos M. Fonseca,et al.  Adaptive active vibration control using genetic algorithms , 1995 .

[15]  Abdelhafid Bayadi Parameter identification of ZnO surge arrester models based on genetic algorithms , 2008 .

[16]  Ali Ghaffari,et al.  Identification and control of power plant de-superheater using soft computing techniques , 2007, Eng. Appl. Artif. Intell..

[17]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[18]  H.-X. Li,et al.  Identification of Hammerstein models using genetic algorithms , 1999 .

[19]  Vu Duong,et al.  System identification by genetic algorithm , 2002, Proceedings, IEEE Aerospace Conference.

[20]  Hillol Kargupta,et al.  System Identification with Evolving Polynomial Networks , 1991, ICGA.