Adaptive Auto-Guidance Control for Variable-State Agricultural Vehicles
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Solution Overview
Problem
Existing auto-guidance controllers for agricultural vehicles are resource-intensive to calibrate and tune for various machine states and operating modes, leading to sub-optimal performance due to the need for universal controller gains across diverse conditions.
Innovation Solution
An auto-guidance system that dynamically adjusts based on real-time sensor information about machine states and operating modes, using sensors to update the controller for specific conditions, optimizing performance by relaxing robustness constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If universal controller gains are used across diverse machine states and operating modes, then the auto-guidance controller can operate in all conditions, but performance becomes sub-optimal due to resource-intensive calibration requirements
Solution Approach 1:
The patent applies dynamics by transitioning from static universal controller gains to dynamic adaptive controller gains that automatically adjust based on real-time sensor feedback about machine state and operating mode. The controller dynamically selects appropriate gain values from predefined sets corresponding to different operating conditions, eliminating the need for manual recalibration while optimizing performance for each specific state.
Solution Approach 2:
The patent implements parameter changes by modifying controller gain parameters based on detected machine state and operating mode. Different sets of controller gains are predefined for different operating conditions (e.g., attached vs. detached implement states), and the system automatically switches between these parameter sets to optimize guidance performance without requiring resource-intensive recalibration.
2Reliability
If the controller is calibrated and tuned for various machine states, then performance can be optimized for each condition, but the calibration process becomes resource-intensive
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple sets of controller gains corresponding to different machine states and operating modes during the design phase. These predefined gain sets are prepared in advance for various anticipated conditions (e.g., different implement weights, operating modes), allowing the controller to immediately select the appropriate pre-optimized parameters without requiring real-time calibration or tuning.
Solution Approach 2:
The system implements self-service by automatically detecting the current machine state and operating mode through sensors, then autonomously selecting the appropriate controller gain set without requiring external calibration input. The controller serves itself by making real-time parameter adjustments based on sensor feedback, eliminating the need for resource-intensive manual calibration processes.
3Productivity
If sensor information is used to dynamically update the controller, then performance is optimized for current conditions, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the control system into distinct functional modules: sensor modules for detecting specific machine states, a processing module for determining operating mode based on sensor inputs, and a control module for selecting appropriate gain sets. This modular segmentation manages complexity by organizing the dynamic update process into discrete, manageable components with clear interfaces.
Solution Approach 2:
The patent implements universality by creating a multi-functional controller architecture that can handle multiple machine states and operating modes through a single unified system. The same sensor suite and controller structure serve multiple functions by detecting different parameters (implement attachment state, operating mode) and selecting from multiple predefined gain sets, reducing overall system complexity compared to having separate controllers for each condition.
Data Source
AI summary
A system and a method for controlling an agricultural vehicle includes receiving sensor information for the agricultural vehicle from one or more sensors, determining a change in a state of the agricultural vehicle based on the sensor information, determining a change in a physical parameter of the agricultural vehicle based on the change in the state of the agricultural vehicle, updating an auto-guidance controller for the agricultural vehicle based on the change in the physical parameter of the agricultural vehicle, and controlling an operation of the agricultural vehicle based on the updated auto-guidance controller.