Parameter identification of a nonlinear model: replicating the motion response of an autonomous underwater vehicle for dynamic environments
Randeni P, SAT and Forrest, AL and Cossu, R and Leong, ZQ and Ranmuthugala, SD and Schmidt, V, Parameter identification of a nonlinear model: replicating the motion response of an autonomous underwater vehicle for dynamic environments, Nonlinear Dynamics, 91, (2) pp. 1229-1247. ISSN 0924-090X (2018) [Refereed Article]
Copyright Springer Science-Business Media B. V. 2017
This study presents a system identification algorithm to determine the linear and nonlinear parameters of an autonomous underwater vehicle (AUV) motion response prediction mathematical model, utilising the recursive least squares optimisation method. The key objective of the model, which relies solely on propeller thrust, gyro measurements and parameters representing the vehicle hydrodynamic, hydrostatic and mass properties, is to calculate the linear velocities of the AUV in the x, y and z directions. Initially, a baseline mathematical model that represents the dynamics of a Gavia class AUV in a calm water environment was developed. Using a novel technique developed in this study, the parameters within the baseline model were calibrated to provide the motion response in different environmental conditions by conducting a calibration mission in the new environment. The accuracy of the velocity measurements from the calibrated model was substantially greater than those from the baseline model for the tested scenarios with a minimum velocity prediction improvement of 50%. The determined velocities will be used to aid the inertial navigation system (INS) position estimate using a Kalman filter data fusion algorithm when external aiding is unavailable. When an INS is not externally aided or constrained by a mathematical model such as that presented here, the positioning uncertainty can be more than 4% of the distance travelled (assuming a forward speed of 1.6 ms−1) . The calibrated model is able to compute the position of the AUV within an uncertainty range of around 1.5% of the distance travelled, significantly improving the localisation accuracy.
autonomous underwater vehicles, AUVs, system identificatoin, recursive least squares optimisation, mathematical models, underwater location, model-aided inertial navigation system