Open AccessModelling of Flexible Manipulator System via Ant Colony Optimization for Endpoint Acceleration
Author(s) -
Siti Sarah Zahidah Nazri,
Muhamad Sukri Hadi,
Hanim Mohd Yatim,
Mat Hussin Ab Talib,
Intan Zaurah Mat Darus
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2129/1/012016
Subject(s) - control theory (sociology) , ant colony optimization algorithms , inertia , robustness (evolution) , acceleration , vibration , autoregressive model , ant colony , computer science , flexibility (engineering) , population , stability (learning theory) , mean squared error , engineering , simulation , mathematics , algorithm , artificial intelligence , statistics , control (management) , machine learning , biochemistry , chemistry , physics , demography , classical mechanics , quantum mechanics , sociology , gene
The application of flexible manipulators has increased in recent years especially in the fourth industrial revolution. It plays a significant role in a diverse range of fields, such as construction automation, environmental applications, space engineering and many more. Due to the lightweight, lower inertia and high flexibility of flexible manipulators, undesired vibration may occur and affect the precision of operation. Therefore, development of an accurate model of the flexible manipulator was presented prior to establishing active vibration control to suppress the vibration and increase efficiency of the system. In this study, flexible manipulator system was modelled using the input and output experimental data of the endpoint acceleration. The model was developed by utilizing intelligence algorithm via ant colony optimization (ACO), commonly known as a population-based trail-following behaviour of real ants based on autoregressive with exogenous (ARX) model structure. The performance of the algorithm was validated based on three robustness methods known as lowest mean square error (MSE), correlation test within 95% confidence level and pole zero stability. The simulation results indicated that ACO accomplished superior performance by achieving lowest MSE of 2.5171×10 −7 for endpoint acceleration. In addition, ACO portrayed correlation tests within 95% confidence level and great pole-zero stability.