The parameter identification of the autonomous underwater vehicle based on multi-innovation least squares identification algorithm

Abstract

An accurate model is important for the engineer to design a robust controller for the autonomous underwater vehicle. There are two factors that make the identification difficult to get accurate parameters of an AUV model in practice. Firstly, the autonomous underwater vehicle model is a coupled six-degrees-of-freedom model, and each state of the kinetic model influences the other five states. Secondly, there are more than 100 hydrodynamic coefficients which have different effects, and some parameters are too small to be identified. This article proposes a simplified six-degrees-of-freedom model that contains the essential parameters and employs the multi-innovation least squares algorithm based on the recursive least squares algorithm to obtain the parameters. The multi-innovation least squares algorithm leverages several past errors to identify the parameters, and the identification results are more accurate than those of the recursive least squares algorithm. It collects the practical data through an experiment and designs a numerical program to identify the model parameters. Meanwhile, it compares the performances of the multi-innovation least squares algorithm with those of the recursive least squares algorithm and the least square method, the results show that the multi-innovation least squares algorithm is the most effective way to identify parameters for the simplified six-degrees-of-freedom model.

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

“…e multi-innovation technique is an effective tool to enhance the convergence rate of estimation methods. at is why it is joined with different estimation Complexity algorithms such as gradient algorithm and least-square method in its recursive and iterative form [33,[105][106][107][108][109][110][111][112][113]. Particularly, it is well known that recursive gradient algorithm presents a slow convergence rate compared to other estimation approaches [113].…”

Linear matrix genetic programming as a tool for data-driven black-box control-oriented modeling in conditions of limited access to training data

“…In the first case, the parameter estimation can be performed using data obtained from simulations 13 – 15 or from measurements of the reaction forces when the robot is moving. The adjustment of the model parameters to the measurement data takes place with the use of the least squares algorithm 16 , 17 or PSO 18 .…”

Parameter identification of an open-frame underwater vehicle based on numerical simulation and quantum particle swarm optimization

“…The constrained model test method is a relatively precise approach for identifying underwater vehicle parameters. However, it suffers from drawbacks such as long model production cycles and high costs [4] .…”

“…The experimental data identification method is to use the underwater vehicle data recorded in the experiment. Firstly, the data is pre-processed, and then the system identification of the underwater vehicle is completed by the least squares algorithm, neural network algorithm, genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and so on [4][5][6] . Wang et al utilized experimental data to construct an underwater vehicle model using long short-term memory (LSTM) and Q-learning [7] , which is a nonparametric identification method.…”