Agricultural Implement Sequence Control With Sensor-Based Optimization
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Solution Overview
Problem
Existing sequence control systems for agricultural machines rely on operator skill levels for recording sequence steps, leading to suboptimal performance when executed automatically, especially if recorded by unskilled operators.
Innovation Solution
An agricultural machine equipped with a control unit that records and optimizes sequence steps by comparing sensor signals with reference values to detect poor operations, adjusting commands and parameters to mitigate maloperations, and utilizing AI algorithms for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sequence steps are recorded based on manual operations by operators, then the sequence control can be executed automatically, but the performance depends on the skill level of the operator and may not provide optimal performance
Solution Approach 1:
The control unit receives sensor signals during sequence execution and compares them with reference values to detect poor operations. This feedback mechanism allows the system to identify when an operator's manual input deviates from optimal performance, enabling subsequent optimization of the recorded sequence steps to compensate for poor operational inputs.
Solution Approach 2:
The system records reference values during a teach mode before automatic execution. These reference values represent optimal operational parameters that are stored and used for comparison during subsequent executions. By establishing these reference values in advance, the system can detect and correct deviations caused by unskilled operators during automatic playback.
2Ease of operation
If sequence steps are recorded by unskilled operators, then the system is easier to operate, but the execution may include maloperations such as collisions or excessive vibrations
Solution Approach 1:
Sensors monitor the actual execution of sequence steps and provide feedback to the control unit. When sensor signals indicate maloperations such as collisions or excessive vibrations, the system detects these harmful factors and optimizes the recorded sequence steps to prevent recurrence, thereby eliminating the need for high operator skill while maintaining safe operation.
Solution Approach 2:
The system converts the harmful effects of poor operator input into beneficial optimization data. By detecting maloperations through sensor feedback, the system learns from these errors and automatically adjusts the sequence steps to compensate for them, transforming the negative impact of unskilled operation into improved automated performance.
3Reliability
If the system monitors and optimizes sequence steps in real-time, then the performance is improved, but the device complexity increases
Solution Approach 1:
The control unit performs self-optimization by automatically comparing sensor signals with reference values and adjusting sequence steps without external intervention. The system serves itself by detecting poor operations and autonomously optimizing its own control parameters, reducing the need for complex external monitoring systems while improving execution quality.
Solution Approach 2:
The control unit serves multiple functions: it records sequence steps, executes them automatically, monitors sensor signals, compares data with reference values, and optimizes parameters. By consolidating these diverse functions into a single control unit, the system achieves improved reliability without proportionally increasing overall device complexity.
Data Source
AI summary
An agricultural machine has an implement, an actuator, a human machine interface for manually controlling the actuator, a control unit and at least one sensor. The control unit is configured to execute a method for a sequence control to optimize a sequence step of the sequence control in case of a poor operation of the implement.