Open AccessGenetic Programming Based Identification of an Overhead Crane
Author(s) -
Tom Kusznir,
J. Smoczek
Publication year - 2021
Publication title -
journal of konbin
Language(s) - English
Resource type - Journals
eISSN - 2083-4608
pISSN - 1895-8281
DOI - 10.2478/jok-2021-0038
Subject(s) - genetic programming , overhead (engineering) , genetic algorithm , identification (biology) , payload (computing) , mathematical optimization , lagrange multiplier , computer science , control theory (sociology) , dynamic programming , graph , pareto principle , engineering , mathematics , artificial intelligence , control (management) , network packet , computer network , botany , theoretical computer science , biology , operating system
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.