Design of fuzzy logic control system incorporating human expert knowledge for combine harvester

  • Authors:

  • Mahmoud Omid;Majid Lashgari;Hossein Mobli;Reza Alimardani;Saeid Mohtasebi;Reza Hesamifard

  • Affiliations:

  • Department of Agricultural Machinery, College of Agriculture and Natural Resources, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran;Faculty of Agriculture, University of Arak, Arak, Iran;Department of Agricultural Machinery, College of Agriculture and Natural Resources, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran;Department of Agricultural Machinery, College of Agriculture and Natural Resources, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran;Department of Agricultural Machinery, College of Agriculture and Natural Resources, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran;Sharif University of Technology, Tehran, Iran

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2010

Quantified Score

Hi-index 12.05

Visualization

Abstract

Many factors affect the yield loss in wheat harvesting with a grain combine harvester. Grain harvesting is a non-linear process, is of considerable complexity, and there is no mathematical model to describe the behavior of this complex system. In this paper, a fuzzy logic controller (FLC) incorporating human expert knowledge is designed for automatic adjustment and control of the harvester to achieve minimal grain losses especially at the position of straw walker and upper sieve. The FLC automatically adjusts cylinder speed, concave clearance, fan speed and forward speed of the combine based on the measured losses at straw walker and sieve sections. The designed FLC expert system consists two inputs and four outputs. Trapezoidal membership functions were selected for input fuzzy linguistic variables (straw walker and sieve losses), whereas fuzzy singletons were considered for the outputs. Based on human expert knowledge, six rules with logical AND operator and Mamdani implication are extracted. FLC was implemented in a programmable logic controller (PLC). Field experiments in two different irrigated or non-irrigated cultivated areas in order to evaluate the system. It was found the losses at the position of straw walker and upper sieve in the irrigated wheat cultivated area is much higher than the dry wheat cultivation area. Statistical analysis using t-test was also indicated a significant difference (p