An Artificial Neural Network Modeling for Force Control System of a Robotic Pruning Machine

  • Ali Hashemi Department of Horticulture, Faculty of Agriculture and Natural Resources Persian Gulf University, Bushehr, Iran
  • Keyvan Asefpour Vakilian Department of Agrotechnology, College of Abouraihan University of Tehran, Tehran, Iran
  • Javad Khazaei Department of Agrotechnology, College of Abouraihan University of Tehran, Tehran, Iran
  • Jafar Massah Department of Agrotechnology, College of Abouraihan University of Tehran, Tehran, Iran
Keywords: power consumption, sensitivity coefficient, back-propagation, rotational speed

Abstract

Nowadays, there has been an increasing application of pruning robots for planted forests due to the growing concern on the efficiency and safety issues. Power consumption and working time of agricultural machines have become important issues due to the high value of energy in modern world. In this study, different multi-layer back-propagation networks were utilized for mapping the complex and highly interactive of pruning process parameters and to predict power consumption and cutting time of a force control equipped robotic pruning machine by knowing input parameters such as: rotation speed, stalk diameter, and sensitivity coefficient. Results showed significant effects of all input parameters on output parameters except rotational speed on cutting time. Therefore, for reducing the wear of cutting system, a less rotational speed in every sensitivity coefficient should be selected.

Published
2014-06-26
Section
Articles