Petri Based Recurrent Fuzzy Neural Control for SY-II Remote Operated Vehicle
Recurrent fuzzy neural network is widely applied in many areas because it combines the advantages of low level learning and high level reasoning,in considering the complicated factors and requirements for the Remote Operated Vehicle(ROV) control,petri network has been introduced to design a dynamic controller for underwater robot.It intends to reduce the computation burdens during network parameters learning.The gradient descent method has been used for online training.In order to guarantee its convergence,we have used the discrete Lyapunov function to determine its learning rate.The tank experiments have proved that the controller can adjust control quantity to reduce caculation and present strong advantages in the ROV robustness control.
Remote Operated Vehicle Petri Network(PN) Recurrent Fuzzy Neural Network(RFNN)
Huang Hai Wan Lei Zhang Guocheng Pang Yongjie
Key Laboratory of Science and Technology for National Defense of Autonomous Underwater Vehicle Harbin Engineering University Harbin, China
国际会议
沈阳
英文
349-353
2012-07-27（万方平台首次上网日期，不代表论文的发表时间）