DRFNN-Adaptive Output Feedback Controller for Depth Tracking of AUV
This paper addresses the problem of autonomous underwater vehicle (AUV) depth control in the absence of full state information. Only position message permitted as input vector of controller designing process. An observer based on dynamic recurrent fuzzy neural network (DRFNN) is designed to estimate the other states of diving dynamics, where the DRFNN is adopted to evaluate the dynamic complex nonlinear part which is caused by the hard accurate estimation of the hydrodynamic coefficients and the nonlinear structure in the pitch motion of an AUV. An adaptive output feedback controller is designed based on the developed observer using the observer backstepping technique. The proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded. Simulation studies illustrate the effectiveness of the proposed control scheme.
ZHANG Li-Jun QI Xue ZHAO Jie-Mei JIA He-Ming PANG Yong-Jie
College of Automation, Harbin Engineering University, Harbin 150001, P.R.China College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-6
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）