SYSTEM IDENTIFICATION OF DYNAMICS OF UNDERWATER VEHICLE USING ARTIFICIAL NEURAL NETWORKS

海洋结构物、船舶和水下航行器（如载人潜水器）的动力学是复杂的、高度非线性的，用传统的动力学理论难以考虑，特别是当它们以一定的低速运行时。此外，在操作期间动态可能改变。为了处理这样一个复杂的和时变的动态，神经网络I/O系统是有利的，即使在输入和输出是多个利用学习能力。本研究以人工神经网络作为控制器与辨识模型的结合，并根据辨识模型的输入输出关系自适应地修正控制器，提出一种前馈神经网络的结构及其学习过程，以模拟被控对象的动态行为。该网络包括两种循环连接，即，从输出层r到输入层r以及从隐藏层r到输入层r。第一种连接使网络能够从其自身的输出中获得输入状态变量，第二种连接使网络本身保持过去数据的影响。在本文中，学习过程进行了改进，配备网络的能力，仿真的动态行为，包括高阶有限差分。建议的网络被采用的神经网络为基础的控制系统称为“SONCS：自组织神经网络控制器系统”，它已被开发为水下机器人的自适应控制系统。SONCS中的神经网络控制器可以利用网络的模拟能力进行快速自适应。通过应用于一个多功能机器人“双汉堡”的航向保持控制，成功地证明了网络的效率。

Dynamics of offshore structures, ships, and underwater vehicles such as manned submersibles are complicated and highly nonlinear to be considered with conventional dynamic theories, especially when they are operated in definitely slow speed. Moreover, the dynamics may be changed during operation. In order to deal with such a complex and time varying dynamics, the neural network I/O system is advantageous taking advantage of learning ability even if the input and the output are multiple. In this research, the controller and the identification model consist of the artificial neural network, and the controller is modified adaptively based on the I/O relation of the identification model.This year, a structure of feed forward neural network and its learning process were proposed to simulate the dynamic behavior of the controlled object. The network includes two kinds of recurrent connections, i.e., from the output layr to the input layr and from the hidden layr to the input layr. The first connection enables the network to obtain the input state variables from its own outputs and the second one is to keep the influence of the past data in itself. In this paper, the learning process is improved to equip the network with the capability of emulating the dynamic behavior including higher-order finite differences. The proposed network is adopted to the neural-network-based control system called "SONCS : Self-Organizing Neural-net Controller System" , which has been developed as an adaptive control system for Underwater Robots. The neural network controller in SONCS can be quickly adapted taking advantage of the network's simulating ability. The efficiency of the network is successfully demonstrated through the application to heading keeping control of a versatile robot called "Twin-Burger".