SONCS: Self-organizing neural-net-controller system for autonomous underwater robots
SONCS: Self-organizing neural-net-controller system for autonomous underwater robots
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SONCS：用于自主水下机器人的自组织神经网络控制器系统
DOI：
10.1109/ijcnn.1991.170670
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发表时间：
1991
期刊：
影响因子：
--
通讯作者：
T. Ura
中科院分区：
文献类型：
--
作者：
T. Fujii;T. Ura
自组织神经网络控制器系统是一种基于神经网络的自适应控制系统。该系统包括一个被称为前向模型网络的多层神经网络，它代表被控对象的动力学。其基本思想是使控制器具有评估和自适应机制，以便用前向模型估计对象的运动。对于前向模型，提出了一种从隐含层到输入层具有递归连接的多层神经网络。以一个非线性系统为建模对象，研究了该正向模型的特性。所提出的网络可用于在广泛的频率范围内进行估计。将自适应神经网络控制系统应用于小型水下机器人的控制问题，并通过自由水槽试验对其性能进行了检验。
The self-organizing neural-net-controller system (SONCS) is introduced as a neural network based adaptive control system. The system includes a multilayered neural network called a forward model network which represents the dynamics of the controlled object. The basic idea is to adapt the controller with the evaluation and adaptation mechanism for estimating the object motion with the forward model. A multilayered neural network which has recurrent connections from the hidden layer to the input layer is proposed for the forward model. Characteristics of this forward model are investigated using a nonlinear system as a modeled object. The proposed network is available for estimation over a wide range of frequency. The SONCS was applied to the control problem of a small underwater robot, and its performance was examined through free-swimming tank tests.<<ETX>>