Predictive Machine Control for Future Field Conditions
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Solution Overview
Problem
Current control systems for mobile machines, such as agricultural vehicles, operate reactively, relying on real-time sensor data to adjust settings, which limits their ability to dynamically optimize machine performance based on anticipated future conditions.
Innovation Solution
A predictive control system that uses a policy map with prescribed machine settings and a predictive model to anticipate future conditions, allowing for adjustments to be made to optimize machine performance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reactive control systems are used that rely on real-time sensor data to adjust settings, then the system responds to current conditions, but the ability to dynamically optimize machine performance based on anticipated future conditions is limited
Solution Approach 1:
The control system performs preliminary actions by using sensor data to predict future conditions and pre-adjusting machine settings before those conditions are actually encountered. This allows the machine to be proactively optimized for upcoming terrain, operational changes, or environmental conditions rather than merely reacting to them after they occur.
Solution Approach 2:
The system implements feedback by continuously monitoring current machine operation and external conditions through sensors, comparing actual performance against desired performance, and using this information to adjust control settings. This closed-loop feedback mechanism enables dynamic optimization of machine performance based on both current and predicted future states.
2Productivity
If predictive control is implemented to optimize machine performance, then productivity improves, but device complexity increases
Solution Approach 1:
The control system uses an intermediary predictive model or algorithm that processes sensor data and translates it into optimal control settings. This intermediary layer simplifies the complexity by providing a structured method for predicting future conditions and determining appropriate adjustments, rather than requiring complex direct control of all machine parameters.
Solution Approach 2:
The system optimizes machine performance by dynamically changing control parameters and settings based on predicted future conditions. Rather than redesigning the entire control architecture, the system adjusts existing parameters such as speed, power output, and operational modes in response to predictions, thereby improving productivity with minimal increase in overall system complexity.
Data Source
AI summary
A policy map is obtained that includes of prescribed machine settings values for controlling a machine to perform an operation at different locations in the field. Sensor signals indicating conditions that will be encountered by the machine in the future, as it moves through the site. A predictive model provides an expected machine response, based upon the a future condition and a prescribed machine setting value. Adjusted machine setting value is generated based on the expected machine response. Control signals are generated, based on the adjusted machine setting value, to control a set of controllable subsystems.