Adaptive RBFNN based Fuzzy Sliding Mode Control for Trajectory Tracking of Underwater Manipulator in Condenser
Adaptive radial basis function neural network (RBFNI) based fuzzy sliding mode controller for trajectory tracking of underwater manipulator in condenser is proposed. The RBFNN is used to approximate the manipulator system由namics, the weights of the RBFNN are changed according to adaptive algorithm to hit the sliding surface and slide along it. In order to guarantee the stability and the convergence of the system, the sliding mode control gain is adjusted byadaptive fuzzy system to compensate the network approximation error and the external disturbances. The simulation results demonstrate that the proposed control scheme is feasible and effective.
机器人 神经网络 滑模控制
Fan Shaosheng Liu Fei
Changsha University of Science and Technology, 410004, Changsha, China
国内会议
长沙
英文
1-8
2011-11-02（万方平台首次上网日期，不代表论文的发表时间）