Nonlinear system identification on a combine harvester

Abstract

The traction system of a combine harvester contains considerable nonlinearities. The objective of this paper is to derive a model of the propulsion which can then be used for regulator design. First the nonlinearities are quantified by analyzing the output of the system excited by a multisine. Standard linear system identification techniques (such as ARX and ARMAX) are then compared to a more recent nonlinear state-space technique. Finally the derived models are validated on several alternative input signals.

Research on Optimal Control Algorithm for Power Characteristics Segmentation of Forage Harvester

“…Kotyk et al studied the load detection and feedback technology of the feeding amount of agricultural machinery, which reduced the operation power consumption and locked rotor failure rate, and improved the production efficiency (Kotyk et al, 1991). Coen and others used fuzzy technology to build a control framework, and realized automatic operation of the combine based on model predictive control technology, reducing labour intensity (Coen et al, 2006). Baruah and others built the mathematical model of the whole machine based on the overall energy consumption (Baruah et al, 2005); Reddy et al fused explicit knowledge of design and implicit knowledge of design intent from the perspective of mechanical modeling technology of intelligent agricultural machinery to improve the flexibility, adaptability and reusability of the model (Reddy et al, 2018).…”

Velocity control system with variable universe adaptive fuzzy-PD method for agricultural vehicles

“…Coen et al developed a cruise control system on combine harvester by controlling the engine speed and the pump setting, derived the nonlinear model of the propulsion system and designed a model-based predictive controller [2][3][4]. W. Lin et al also developed a walking velocity control system based on feed quantity on combine harvester [5].…”