Neural networks for self-learning control systems
作者:
DERRICKH. NGUYEN,
BERNARD WIDROW,
期刊:
International Journal of Control
(Taylor Available online 1991)
卷期:
Volume 54,
issue 6
页码: 1439-1451
ISSN:0020-7179
年代: 1991
DOI:10.1080/00207179108934220
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Neural networks can be used to solve highly nonlinear control problems. This paper shows how a neural network can learn of its own accord to control a non-linear dynamic system. An emulator, a multilayered neural network, learns to identify the system's dynamic characteristics. The controller, another multilayered neural network, next learns to control the emulator. The self-trained controller is then used to control the actual dynamic system. The learning process continues as the emulator and controller improve and track the physical process. An example is given to illustrate these ideas. The ‘truck backer-upper’, a neural network controller steering a trailer truck while backing up to a loading dock, is demonstrated. The controller is able to guide the truck to the dock from almost any initial position. The technique explored here should be applicable to a wide variety of non-linear control problems.
点击下载:
PDF (423KB)
返 回