Adaptive Auto-Guidance Control for Variable-State Farm Vehicles
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Solution Overview
Problem
Typical auto-guidance controllers for agricultural vehicles are resource-intensive to calibrate and provide sub-optimal performance due to their reliance on universal gains and algorithms that fail to account for specific machine states and operating modes, leading to inefficient dynamic adjustments.
Innovation Solution
An auto-guidance system that dynamically updates the controller using real-time sensor information about the vehicle's state and physical parameters, such as mass, moment of inertia, and center of gravity, to optimize performance for current conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If universal gains and algorithms are used in auto-guidance controllers, then the controller can be applied to various agricultural vehicles, but the performance is sub-optimal and extensive calibration is required
Solution Approach 1:
The auto-guidance controller dynamically adjusts control parameters based on real-time sensor information about vehicle state (mass, moment of inertia, center of gravity). This transforms the static universal controller into a dynamic adaptive system that automatically optimizes performance for current operating conditions without requiring extensive calibration.
2Adaptability or versatility
If universal gains and algorithms are used in auto-guidance controllers, then the controller can be applied to various agricultural vehicles, but extensive calibration is required
Solution Approach 1:
The controller performs self-calibration by automatically determining physical parameters from sensor information about vehicle state. The system serves itself by extracting necessary calibration data from operational sensors rather than requiring manual calibration procedures, thereby eliminating time loss during deployment.
Solution Approach 2:
The system preliminarily determines physical parameters (mass, moment of inertia, center of gravity) from sensor data before executing control operations. This preliminary characterization of vehicle properties enables the controller to be immediately operational without requiring subsequent calibration steps.
3Measurement precision
If real-time sensor information is used to update the controller, then the accuracy and robustness of vehicle control is enhanced, but the system complexity increases
Solution Approach 1:
The system implements feedback by continuously monitoring vehicle state through sensors and using this information to update control parameters in real-time. This closed-loop approach enhances measurement precision and control accuracy while managing complexity through systematic feedback integration rather than ad-hoc adjustments.
Data Source
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AI summary
A system and a method (600) for controlling an agricultural vehicle (12) includes receiving sensor information for the agricultural vehicle (12) from one or more sensors (120), determining a change in a state of the agricultural vehicle (12) based on the sensor information, determining a change in a physical parameter of the agricultural vehicle (12) based on the change in the state of the agricultural vehicle (12), updating an auto-guidance controller for the agricultural vehicle (12) based on the change in the physical parameter of the agricultural vehicle (12), and controlling an operation of the agricultural vehicle (12) based on the updated auto-guidance controller.