Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)

  • Authors

    • Ainul, H.M.. Y
    • Salleh, S. M
    • Halib, N
    • Taib, H.
    • Fathi, M. S
  • Modeling, objective function, system identification, validation.
  • System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.

  • References

    1. [1] Abo-Hammour Z, Alsmadi O, Momani S, Arqub OA (2013), A Genetic Algorithm Approach for Prediction of Linear Dynamical Systems. Mathematical Problems in Engineering, 1-12.

      [2] Andrijic ZU, Bolf N, Rolich T (2011), Optimizing Configurable Parameters of Model Structure Using Genetic Algorithm. TEDI – International Interdisciplinary Journal of Young Scientist from the Faculty of Textile Technology 01, 49-54.

      [3] Angelova M & Pencheva T (2011), Tuning Genetic Algorithm Parameters to Improve Convergence Time. International Journal of Chemical Engineering, 1-7.

      [4] Bhuvaneswari NS, Praveena R, Divya R (2012), System Identification and Modelling for Interacting and Non-Interacting Tank Systems using Intelligent Techniques. International Journal of Information Sciences and Techniques (IJIST) 02, 23-37.

      [5] Chang WD (2007), Nonlinear System Identification and Control Using a Real-Coded Genetic Algorithm. Applied Mathematical Modelling 31, 541-550.

      [6] Cherif I & Fnaiech F (2015), System Identification with a Real-Coded Genetic Algorithm (RCGA). International Journal of Applied Mathematics and Computer Science 25, 863-875.

      [7] Ghaffari A, Chaibakhsh A, Parsa H (2007), An Optimization Approach Based on Genetic Algorithm for Modeling Benson Type Boiler. American Control Conference.ACC’07 04, 4860-4865.

      [8] Mantri G & Kulkarni NR (2013), Design and Optimization of PID Controller Using Genetic Algorithm. International Journal of Research in Engineering and Technology 02, 926-930.

      [9] Rabbani MJ, Hussain K, Khan A, Ali A (2013), Model Identification and Validation for a Heating System Using MATLAB System Identification Toolbox. IOP Conference Series: Materials Science and Engineering Proceedings of the 1st International Conference on Sensing for Industry, Control, Communications, and Security Technologies, ICSICCST 2013 51, 1-10.

  • Downloads

  • How to Cite

    Y, A. H., M, S. S., N, H., H., T., & S, F. M. (2018). Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA). International Journal of Engineering & Technology, 7(4.30), 443-447.