1. bookVolume 51 (2021): Issue 3 (October 2021)
Journal Details
License
Format
Journal
eISSN
2083-4608
First Published
26 Feb 2008
Publication timeframe
4 times per year
Languages
English
access type Open Access

Genetic Programming Based Identification of an Overhead Crane

Published Online: 30 Oct 2021
Volume & Issue: Volume 51 (2021) - Issue 3 (October 2021)
Page range: 123 - 133
Journal Details
License
Format
Journal
eISSN
2083-4608
First Published
26 Feb 2008
Publication timeframe
4 times per year
Languages
English
Abstract

Overhead cranes carry out an important function in the transportation of loads in industry. The ability to transport a payload quickly and accurately without excessive oscillations could reduce the chance of accidents as well as increase productivity. Accurate modelling of the crane system dynamics reduces the plant-model mismatch which could improve the performance of model-based controllers. In this work the simulation model to be identified is developed using the Euler-Lagrange method with friction. A 5-step ahead predictor, as well as a 10-step ahead predictor, are obtained using multi-gene genetic programming (MGGP) using input-output data. The weights of the genes are obtained by using least squares. The results of 15 different genetic programming runs are plotted on a complexity-mean square error graph with the Pareto optimal solutions shown.

Keywords

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