Agricultural Vehicle Sensor Signal Filtering for Control Stability
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Solution Overview
Problem
Agricultural machines, such as combines, face challenges in controlling operations due to varying criteria like crop type, weather, and soil conditions, leading to noisy and rapidly changing sensor signals that make it difficult to maintain optimal machine settings.
Innovation Solution
The system applies multiple filters to sensor signals, using a long-term filter for a larger set of data points and a short-term filter for recent data, generating control signals to stabilize subsystem operations and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple filters with different data point sets are applied to sensor signals, then control signal stability and noise reduction are improved, but device complexity and processing time increase
Solution Approach 1:
The filtering system is segmented into multiple independent filter units, each processing sensor signals with different data point sets. This allows parallel processing of filtered and unfiltered signals, maintaining stability improvements while reducing computational burden compared to a single complex filter.
Solution Approach 2:
The system applies filtering selectively - not all sensor signals are filtered with all data point sets. Instead, specific filters are applied based on the type of sensor signal and control requirements, reducing overall system complexity while maintaining reliability for critical control parameters.
2Reliability
If multiple filters with different data point sets are applied to sensor signals, then noise reduction and signal stabilization are improved, but processing time and computational load increase
Solution Approach 1:
The filtering process is segmented into parallel processing streams where different filters operate simultaneously on the same sensor input. This parallel architecture reduces total processing time compared to sequential filtering while achieving cumulative noise reduction effects.
Solution Approach 2:
Different filtering strengths are applied to different sensor signals based on their specific noise characteristics and control importance. Critical control signals receive stronger filtering with more data points, while less critical signals receive lighter filtering, optimizing the balance between noise reduction and processing time.
Data Source
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AI summary
A sensor generates a sensor signal indicative of a sensed variable. A first filter is applied to the sensor signal, and filters the sensor signal based on a first set of sensor data, to generate a first filtered signal. A second filter is applied to the sensor signal, based on a second set of sensor data that is greater than the first set of sensor data, to generate a second filtered sensor signal. The first and second filtered sensor signals are compared to generate a control signal that can be used to control a controllable subsystem of a mobile machine.