Improved Parameter Estimation for AUV Subsystems in the Horizontal and Vertical Planes in the Presence of Noise

In this paper, the problem of improved parameter estimation and modelling of two subsystems of Autonomous Underwater Vehicle (AUV) in the horizontal and vertical planes is addressed, in the presence of the measurement noise. To this end, two identification strategies including the adaptive parameter identifier and the error least square (LS) identifier are designed and investigated, as two low-cost methods to identify the system model based on the information, extracted from practical tests. In both of the proposed identifiers, the selection problem of the appropriate values of the identifiers design parameters, as an important factor in order to improve process performance estimation, by using Particle Swarm Optimization (PSO) algorithm. It is shown that, this approach reduces the limitations in the practical methods. By applying the appropriate input signal, the system parameters have been finally given by utilizing both the adaptive identifier and the error least square identifier. Taking into account the measurement noise, it is shown that the latter approach provide better performance concerning the convergene rate and the accuracy. The simulation results are also presented to demonstrate and compare the performance of the mentioned identification schemes.