Mobile Machine Control System for Crop-Specific Performance Targets
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Solution Overview
Problem
Operating complex mobile machines like combines requires significant operator experience and manual adjustments based on varying conditions such as crop type, wind, and terrain, leading to inefficiencies and variability in performance metrics like productivity, fuel efficiency, and material quality.
Innovation Solution
A control system that calculates performance metrics from sensor inputs and stores them as performance targets, allowing for automatic generation of action signals to adjust machine settings, thereby enabling the machine to maintain optimal performance across different conditions without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual control inputs and operator perception are used to operate complex mobile machines, then the machine can be operated with basic control systems and sensors, but the performance metrics vary significantly and require years of operator experience to achieve high performance
Solution Approach 1:
The control system automatically adjusts machine settings based on sensor data and performance targets without requiring manual operator intervention. The system serves itself by autonomously optimizing operational parameters such as fan speed, rotor clearance, and sieve settings based on real-time performance monitoring, thereby eliminating the need for years of operator experience to achieve high productivity
Solution Approach 2:
The system continuously monitors sensor inputs, calculates performance metrics, compares them against stored performance targets, and automatically generates control signals to adjust machine settings. This closed-loop feedback mechanism ensures consistent high performance by constantly optimizing operational parameters based on actual machine performance and predefined targets for different crop types and conditions
2Adaptability or versatility
If operator manual adjustments are made based on varying conditions, then the machine can adapt to different crop types and environments, but the adjustments are time-consuming and lead to variability in performance metrics
Solution Approach 1:
Performance targets for different crop types and operating conditions are pre-calculated and stored in the system before operation begins. When a specific crop type or condition is detected, the system automatically retrieves and applies the corresponding pre-optimized settings, eliminating the need for time-consuming manual adjustments and ensuring immediate adaptation to varying conditions
Solution Approach 2:
The control system autonomously identifies operating conditions through sensor data, selects appropriate performance targets, and automatically adjusts machine settings without operator intervention. This self-service capability enables rapid adaptation to different crop types and environments, significantly reducing the time loss associated with manual reconfiguration
3Productivity
If multiple performance targets are stored for different crop types and conditions, then the machine can maintain optimal performance across varying conditions, but the data storage and retrieval system becomes more complex
Solution Approach 1:
The control system uses a universal data structure and retrieval mechanism that handles multiple performance targets for different crop types and conditions through a single integrated system. The system universally processes sensor inputs, compares performance metrics against stored targets, and retrieves appropriate settings regardless of crop type or operating condition, maintaining consistent high performance without requiring separate complex systems for each scenario
Data Source
AI summary
Machine sensor inputs are received, and a set of performance metrics are calculated based upon the sensor inputs. The set of performance metrics are stored as a performance target along with one or more additional performance targets. One of the performance targets is retrieved and the machine automatically generates an action signal indicative of machine setting adjustments that can be made in order to control operation of the machine to more closely conform to the retrieved performance target.