Self-organizing Fuzzy Neural Tracking Control for Surface Ships with Unmodelled Dynamics and Unknown Disturbances
In this paper,a novel self-organizing fuzzy neural control(SOFNC)scheme for tracking surface ships,whereby a self-organizing fuzzy neural network(SOFNN)is used to approximate unmodelled dynamics and unknown disturbances,is proposed.The salient features of the SOFNC are as follows:(1)Unlike previous fuzzy neural networks(FNN),the SOFNN is able to dynamically self-organize compact T-S fuzzy rules according to structure learning criteria.(2)The SOFNN-based SOFNC scheme is designed by combining the sliding-mode control(SMC)with the improved projection-based adaptive laws which avoid parameter drift.(3)A robust supervisory controller is presented to enhance the robustness to approximation errors.(4)The SOFNC achieves excellent tracking performance,whereby tracking errors and their first derivatives are globally asymptotical stable in addition that all signals are bounded.Simulation studies demonstrate remarkable performance the SOFNC in terms of tracking error and online approximation.
Tracking Control Self-organizing Fuzzy Neural Network Surface Ship
WANG Ning SUN Jingchao LIU Yancheng HAN Min
Marine Engineering College,Dalian Maritime University,Dalian 116026,P.R.China Faculty of Electronic Information and Electrical Engineering,Dalian University of Technology,Dalian
国际会议
The 33th Chinese Control Conference第33届中国控制会议
南京
英文
8859-8864
2014-07-28（万方平台首次上网日期，不代表论文的发表时间）