研究目的
To develop a method for online identification of AUV dynamics via in-field experiments to update the dynamic model when the AUV is reconfigured with different payloads, enabling better performance in control and guidance law.
研究成果
The developed method enables rapid online identification of AUV dynamics, with parameters converging within 12 seconds and models explaining 78% to 92% of the output variation. The SVF-RLS estimator outperforms conventional offline methods in prediction accuracy, computational cost, and training time. The identified models are useful for estimating the AUV's turning radius at different speeds and designing gain-scheduled controllers.
研究不足
The method requires the AUV to perform specific maneuvers for identification, which may not be feasible in all operational scenarios. The accuracy of the identified model depends on the quality of the sensor data and the excitation signals.
1：Experimental Design and Method Selection:
The AUV is commanded to execute a compact set of maneuvers under doublet excitation. The identification process uses a state variable filter and recursive least square (SVF-RLS) estimator to estimate unknown parameters.
2：Sample Selection and Data Sources:
The AUV's response is measured using on-board sensors during the maneuvers.
3：List of Experimental Equipment and Materials:
The STARFISH AUV, equipped with a compass module for attitude measurement, a pressure sensor for depth measurement, and a doppler velocity log (DVL) for speed measurement.
4：Experimental Procedures and Operational Workflow:
The AUV performs straight runs at constant depth and thrust, followed by excitation signals injected into the rudder deflection to excite yaw dynamics. Parameters are estimated online during the training stage and validated using fresh data in the validation stage.
5：Data Analysis Methods:
The SVF-RLS estimator is used for parameter estimation, and the coefficient of determination is calculated to validate the model's prediction capability.
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