Model Identification of a Small Fully-Actuated Aquatic Surface Vehicle Using a Long Short-Term Memory Neural Network

Abstract

A long short-term memory neural network is used to provide a system model that captures the temporal-dynamics of a holonomic, fully-actuated aquatic surface vehicle. As is true in many fields, new developments in robotics often are made in simulation first before being applied to real systems. To simulate an aquatic or aerial robot, a dynamic system model of the robot is required. The more representative the dynamic model is of the real robot, the smaller the simulation-to-reality gap becomes. The performance of the neural network is compared against a classical parametric model, where coefficients of the parametric model were identified using the same data that was used to train the neural network. The results show that the neural network consistently outperforms the classical parametric model and significantly reduces the error between real velocities and estimated velocities. The neural network also demonstrated the ability to capture complex hydrodynamic effects that were not captured in the parametric model. In addition to the performance improvements, the neural network method can be easily adapted to similarly actuated aquatic vehicles by simply retraining, whereas the classical approach would require manual selection of new equation terms. The neural network model that was created has been used in a vehicle simulation and is presently being used as a research tool.

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

“…This method establishes an optimal mapping relationship between input and output data, bypassing the need for any prior physical knowledge of the explicit mathematical model that reflects the system's dynamic characteristics. Artificial neural networks (ANNs) are commonly employed in black-box modeling [35,36], such as two-layer fully connected neural networks [37], three-layer feedforward neural networks with Chebyshev orthogonal basis functions [38], generalized ellipsoidal basis function fuzzy neural networks [39], long short-term memory (LSTM) [40], recursive neural networks [41], and deep learning networks [42]. These architectures aim to map the dynamic relationship between input state variables and output variables such as hydrodynamic force and moment, identifying nonlinear functions in the process.…”