Adaptive Ship Autopilot Parameter Tuning for Heading Stability
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Solution Overview
Problem
Existing rudder angle control systems for ships rely on fixed parameters, which are unsuitable for varying cargo weights and weather conditions, leading to inaccurate control and excessive steering, and previous solutions complicate adjustments and require long periods of data collection or estimator construction.
Innovation Solution
An automatic steering device with an acquiring module, evaluating modules, and a control parameter setting module that calculates and adjusts control parameters based on ship state and weather conditions, using evaluation values for heading and steering performance to ensure stable control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed control parameters are used for rudder angle control, then the control system is simple to operate, but the control accuracy deteriorates and excessive steering occurs under varying cargo weight and weather conditions
Solution Approach 1:
The control parameters are changed from fixed values to dynamically adjustable values that adapt to varying operating conditions. The system automatically adjusts control parameters based on detected ship behavior patterns and current sea conditions, enabling the control system to maintain high accuracy across different cargo weights and weather conditions without requiring manual reconfiguration.
Solution Approach 2:
The invention changes the control parameters from static fixed values to variable parameters that are automatically adjusted based on ship behavior detection. By monitoring heading changes and rudder angles over time, the system identifies behavior patterns and modifies control parameters accordingly, thereby maintaining optimal control accuracy under varying operational conditions.
2Measurement precision
If control parameters are adjusted after long-period cruise data collection, then the control parameters can be optimized for specific conditions, but the control parameters remain unsuitable during the adjustment period
Solution Approach 1:
The control system performs self-adjustment by automatically detecting ship behavior patterns from real-time operational data and autonomously modifying control parameters without requiring manual intervention or long-period data collection. The system continuously monitors heading and rudder angle data, identifies behavior patterns, and adjusts parameters on-the-fly, eliminating the time loss associated with post-cruise parameter adjustment.
Solution Approach 2:
The system prepares control parameters in advance by detecting behavior patterns and adjusting parameters proactively before suboptimal conditions arise. By continuously monitoring operational data and predicting upcoming behavior patterns, the system pre-adjusts control parameters to maintain optimal performance, avoiding the delay of waiting for long-period data collection to complete.
3Measurement precision
If an estimator is constructed to estimate heading shake caused by waves, then the control algorithm can compensate for wave effects, but the system complexity and parameter adjustment burden increase
Solution Approach 1:
The invention extracts the essential behavior characteristics directly from measured heading and rudder angle data without constructing a separate estimator model for wave effects. By analyzing the actual ship response data to identify behavior patterns, the system bypasses the need for complex theoretical estimators while still achieving accurate compensation for wave-induced heading variations.
Solution Approach 2:
Instead of creating a theoretical estimator model to simulate wave effects, the system creates a behavioral copy by directly observing and recording actual ship responses under various conditions. This empirical approach captures wave effects and other environmental influences through real data patterns, eliminating the need for complex mathematical estimators while maintaining accuracy.
Data Source
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AI summary
An automatic steering device (10) includes an acquiring module (110), a first evaluating module (102), a second evaluating module (104), and a control parameter setting module (105). The acquiring module (110) acquires a traveling state including a heading or a position of a ship, and ship information on the ship. The first evaluating module (102) calculates a first evaluation value (Eψ) that is an evaluation value indicative of a performance for maintaining the heading of the ship or a route based on the traveling state. The second evaluating module (104) calculates a second evaluation value (Eδ) that is an evaluation value related to a ship handling control based on the ship information. The control parameter setting module (105) sets a control parameter related to a motion control of the ship based on the first evaluation value (Eψ) or the second evaluation value (Eδ).