Adaptive Performance Targets for Mobile Machine Control
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Solution Overview
Problem
Existing mobile machine control systems often rely on conservative or static setpoints, which fail to adapt to varying conditions and machine-specific performance metrics, leading to suboptimal performance across different environments and machines.
Innovation Solution
A control system that aggregates sensor signal values over time, identifies a threshold signal value based on the spread of these values, and generates control signals to dynamically adjust machine subsystems, automatically updating setpoints to improve performance iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conservative or static setpoints are used in control systems, then the system can be used in a variety of different conditions and machines, but the machine performance is suboptimal across different environments
Solution Approach 1:
The control system dynamically adjusts setpoints based on aggregated sensor data and performance metrics. Instead of using fixed conservative values, the system continuously learns from operational data and adapts thresholds to optimize machine performance while maintaining reliability across varying conditions.
Solution Approach 2:
The system changes control parameters (setpoints and thresholds) based on aggregated sensor signal values and performance metrics. By analyzing the spread and distribution of sensor data over time, the system automatically adjusts parameters to achieve optimal performance for specific machines and conditions rather than using universal conservative values.
2Measurement precision
If user-manual adjustment of setpoints is required, then the system can be customized for specific conditions, but it requires user input and time to configure
Solution Approach 1:
The control system performs self-configuration by automatically aggregating sensor data, analyzing performance metrics, and generating optimized setpoints without requiring user intervention. The system learns from operational data and autonomously adjusts thresholds, eliminating the time-consuming manual configuration process while maintaining high measurement precision.
Solution Approach 2:
The system continuously monitors sensor signals and performance metrics, using this feedback to automatically refine and adjust setpoints. By incorporating real-time performance data into the decision-making process, the system achieves accurate performance targets without requiring users to manually tune parameters, thus reducing configuration time while maintaining precision.
3Ease of operation
If static control thresholds are used, then the control system is simple to operate, but it cannot adapt to varying conditions and machine-specific performance
Solution Approach 1:
The control system automatically adapts to varying conditions and machine-specific performance by aggregating sensor data and autonomously adjusting control thresholds. This self-adapting capability maintains ease of operation since users do not need to manually reconfigure the system for different conditions, while simultaneously achieving high adaptability through data-driven threshold optimization.
Solution Approach 2:
The system uses continuous feedback from sensor signals and performance metrics to dynamically adjust control thresholds. This feedback mechanism enables the system to adapt to varying conditions automatically, maintaining simplicity of operation since the adaptation occurs autonomously based on real-time data without requiring user intervention or complex configuration.
Data Source
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AI summary
Sensor signal values, indicative of a performance metric, are received and recorded over a given time period. The sensor signal values are aggregated, and a threshold signal value is identified based on the aggregated sensor signal value. A set of control signals, for controlling subsystems on the mobile machine, are generated based on the identified threshold signal value, and the subsystems are controlled based upon the control signals.