A REVIEW OF THE FEED RATE DETECTION AND STABILITY CONTROL METHODS IN COMBINE HARVESTERS

The feed rate is an important index for evaluating the performance of a combine harvester. Determining how to accurately reflect the feed rate during harvesting and establishing a reliable detection model is a major focus of current research and an important basis for the next step of feed rate stable control. This paper provides an overview of feed rate detection methods and stable control techniques for combine harvesters. It reviews methods that estimate the feed rate based on the inclined conveyor extrusion pressure, header power, and threshing unit energy consumption. Additionally, it introduces machine learning-based methods that incorporate multiple influencing factors to predict the feed rate. A comparison of different noise reduction techniques used in feed rate detection is also presented, analyzing their effectiveness. Furthermore, this study examines feed rate control methods in combine harvesters, discussing various control approaches with an emphasis on methods that stabilize the feed rate by adjusting header height and harvester forward speed. In response to the current issues of inadequate detection accuracy in feed rate monitoring, limited adaptability, and instability in control systems, it is pointed out that future research needs to innovate in developing advanced sensor technology, optimizing automatic control algorithms as well as data fusion and analytical methodologies.