Self-adapting UUV Docking via Dynamic Proportional Gain
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Solution Overview
Problem
Autonomous underwater vehicle docking is challenging due to the difficulty in physically connecting UUVs with undersea stations, requiring effective guidance and control systems to navigate and adjust steering gains for precise docking.
Innovation Solution
A method using visual detection and a proportional control law with a novel learning function to adjust steering gains, where the UUV captures images, applies filters, calculates error values, and updates proportional gain based on a custom bipolar logistic function, enabling self-tuning and precise control signals for docking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed proportional gain value is used in the proportional controller, then the control system is simple to implement, but the docking precision and stability deteriorate due to inability to adapt to different docking conditions
Solution Approach 1:
The patent applies the Dynamics principle by transitioning from a fixed proportional gain to a dynamically adaptive gain value. The proportional gain is updated in real-time based on the relationship between the current error value and the rate of error change, allowing the control system to automatically adjust its characteristics during the docking process to maintain optimal performance across varying conditions.
Solution Approach 2:
The patent implements Parameter changes by modifying the proportional gain parameter based on the system's current state. The gain value is adjusted according to the error magnitude and its rate of change, enabling the controller to adapt its behavior dynamically during docking operations to achieve both precision and stability.
2Speed
If the proportional gain is increased to improve response speed, then the docking speed increases, but oscillations increase reducing stability
Solution Approach 1:
The patent resolves this contradiction by making the proportional gain dynamic rather than fixed. The gain automatically increases when the error is large (improving response speed) and decreases when the error is small or changing rapidly (reducing oscillations), thus maintaining both speed and stability throughout the docking process.
Solution Approach 2:
The patent changes the proportional gain parameter based on real-time error characteristics. By adjusting the gain according to the magnitude and rate of change of the error, the system achieves fast response when needed while suppressing oscillations during critical docking phases.
3Stability of the object's composition
If the proportional gain is decreased to reduce oscillations, then stability improves, but the response speed and docking efficiency deteriorate
Solution Approach 1:
The patent applies Dynamics by making the proportional gain adaptive rather than static. The gain value evolves during the docking process, being higher during early stages to maintain efficiency and lower during later stages to ensure stability, thus resolving the trade-off between speed and stability.
Solution Approach 2:
The patent implements Parameter changes by adjusting the proportional gain based on the current docking state. The gain is modified according to error magnitude and rate of change, enabling the system to maintain high efficiency when far from target while ensuring stability during precise approach.
4Manufacturing precision
If manual tuning of proportional gain is performed to optimize performance, then docking precision can be improved, but the complexity of operation and time required increase
Solution Approach 1:
The patent applies the Self-service principle by enabling the proportional gain to self-adjust automatically based on real-time error feedback. The system performs its own tuning by computing the optimal gain value from the relationship between error and rate of error change, eliminating the need for manual intervention while maintaining high docking precision.
Solution Approach 2:
The patent implements Feedback by using the error signal and its rate of change to automatically adjust the proportional gain. This closed-loop adaptation allows the system to learn optimal control parameters during operation, achieving high precision without manual tuning complexity.
Data Source
AI summary
A method for adjusting the proportional gain value of a proportional controller for use in an unmanned underwater vehicle. The invention includes the steps of: recording at an unmanned underwater vehicle a captured image in the direction of travel; applying a first filter to the captured image; calculating a first distance from each pixel in the captured image to a specified target color; finding the selected pixel from the captured image with the minimum distance from the specified target color, normalizing the image coordinates of the selected pixel into normalized image coordinates; passing the normalized image coordinates as an error value to the proportional controller; calculating a rate of error at the proportional controller; updating the proportional gain according at the proportional controller; and applying a control signal at the proportional controller.