A Dynamic Neural Network with Feedback for Trajectory Generation

被引：5

作者：

Atmeh, Ghassan ^{[1
]}

Subbarao, Kamesh ^{[2
]}

机构：

[1] Univ Texas Arlington, Aerosp Syst Lab, Arlington, TX 76019 USA

[2] Univ Texas Arlington, Mech & Aerosp Engn, Arlington, TX 76019 USA

来源：

IFAC PAPERSONLINE | 2016年 / 49卷 / 01期

关键词：

Neural Networks; Dynamic Neural Networks; Recurrent Neural Networks; Trajectory planning; Path planning; BACKPROPAGATION; TIME;

D O I：

10.1016/j.ifacol.2016.03.081

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A Dynamic Neural Network (DNN), comprising of a Recurrent Neural Network (RNN) and two Feedforward Neural Networks (FFNN), is detailed in this paper. This neurodynamic system is used to adaptively solve the problem of trajectory generation; planning a path connecting a locus of points as a function of time. The BAN is designed to exhibit desired dynamic behavior which drives the output FFNN to generate a specific trajectory. To make the system adaptive, a feedback FFNN is introduced to generate inputs to the R\N based on the outputs of the output FFNN. Simulations conducted for generation of different types of trajectories show the capabilities of the DNN with feedback in solving the trajectory generation problem. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：367 / 372

页数：6

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