A Reinforcement Learning Algorithm Based Neural Network Used for Course Angle Control of Remotely Operated Vehicle

Author

Gao, Yan-Zeng ; Ye, Jia-Wei ; Song, Xin ; Shi, Ping-An

Author_Institution

Sch. of Civil Eng. & Transp., South China Univ. of Technol., Guangzhou, China

Volume

1

fYear

2009

fDate

10-11 Oct. 2009

Firstpage

31

Lastpage

34

Abstract

The principal contribution of this paper is designed a general framework for an intelligent control system used in course angle control of remotely operated vehicle (ROV). A control scheme based on reinforcement learning (RL) agent combined with radial basis function (RBF) neural network control algorithm is applied. The effectiveness of the controller is demonstrated through simulations, and implementation issues are discussed. The control law is conceptually simple and computationally easy to implement.

Keywords

learning (artificial intelligence); neurocontrollers; position control; radial basis function networks; remotely operated vehicles; course angle control; neural network; radial basis function; reinforcement learning algorithm; remotely operated vehicle; Application software; Computational modeling; Computer networks; Control systems; Learning; Mathematical model; Military computing; Neural networks; Remotely operated vehicles; Sliding mode control; course angle control; radial basis function; reinforcement learning; remotely operated vehicle;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on

Conference_Location

Changsha, Hunan

Print_ISBN

978-0-7695-3804-4

Type

conf

DOI

10.1109/ICICTA.2009.15

Filename

5287717