Harvester Speed Control Using Crop Density Forecast Feedback
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Solution Overview
Problem
Existing agricultural harvesting machines face challenges in efficiently controlling forward drive speed due to inaccuracies in predicting crop stand density, leading to potential overloading and reduced processing quality or blockage, especially when there are errors in predicting crop throughput.
Innovation Solution
A system that includes a controller with a function device and a solution device to create a cost function and optimization problem, taking into account both expected and measured crop stand densities, and generates sequences of control commands to minimize the cost function while considering uncertainty in predictions, ensuring the harvesting machine operates within optimal parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the forward drive speed is increased to improve productivity, then the harvesting machine can process more crop per unit time, but the risk of overloading and blockage increases when prediction errors occur
Solution Approach 1:
The system performs preliminary actions by calculating a forecast of crop stand density before the harvesting machine reaches those locations, using measured data from previously harvested areas. This advance prediction allows the control system to prepare appropriate speed adjustments, preventing overloading before it occurs by proactively adapting the forward drive speed based on anticipated crop conditions.
Solution Approach 2:
The system implements feedback by continuously measuring the actual crop stand density in previously harvested areas, comparing it with the forecasted values, and using this information to refine future predictions. This feedback loop enables the system to learn from past performance and improve the accuracy of forward predictions, thereby enhancing both productivity and reliability.
2Reliability
If the forward drive speed is reduced to avoid overloading, then processing reliability is improved, but productivity decreases
Solution Approach 1:
The system applies dynamics by continuously and dynamically adjusting the forward drive speed based on real-time measurements and forecast updates. Rather than maintaining a fixed conservative speed, the system adaptively modifies the speed profile as new measurement data becomes available, allowing the machine to operate at optimal speeds when conditions permit while preventing overloading when crop density increases are predicted.
Solution Approach 2:
The system changes parameters by adjusting the forward drive speed as a dynamic variable rather than a constant. The control system modifies speed parameters based on the evolving forecast accuracy and actual measured crop stand density, enabling the harvesting machine to optimize its operating parameters in response to changing field conditions.
3Device complexity
If a simple prediction method is used for crop stand density, then the system complexity is reduced, but the accuracy of throughput prediction deteriorates
Solution Approach 1:
The system segments the field into previously harvested areas and future areas, treating them differently. The forecast model uses measured data from segmented past areas to predict conditions in future segments. This segmentation allows the use of simple measurement methods in already-harvested zones while applying predictive modeling for upcoming areas, balancing complexity and accuracy.
Solution Approach 2:
The system introduces an intermediary forecast model that bridges the gap between simple measurements and accurate predictions. Rather than directly measuring crop stand density in future areas (which would require complex sensing), the forecast acts as an intermediary that translates past measurement data into predictions for future conditions, achieving reasonable accuracy without excessive complexity.
4Measurement precision
If real-time measurements are continuously taken to improve prediction accuracy, then the precision of crop stand density is improved, but the time required for processing increases
Solution Approach 1:
The system implements periodic action by updating the forecast and re-calculating the optimization problem at discrete intervals rather than continuously. Measurements are taken periodically as the machine moves through the field, and the control system updates its predictions and speed adjustments at these periodic intervals, balancing measurement precision with acceptable processing time delays.
Data Source
AI summary
A system for the automatic control of a forward drive speed of a harvesting machine comprising: a controller configured with a function device and a solution device, wherein the function device is configured, in chronologically successive steps, taking into account a location-dependent forecast for an expected crop stand density and a measured crop stand density in the harvesting machine to create a cost function and an associated optimization problem with the effect of optimizing a state of the harvesting machine and the solution device is configured to provide respective chronologically successive first sequences of control variables relating to a pre-definition of the forward drive speed, which solve the respective optimization problem and minimize the associated cost function.