Robust H-infinity tracking-control for robotic system based on recurrent fuzzy-neural-networks
基于递归模糊神经网络的机器人鲁棒H∞跟踪控制

Using recurrent fuzzy-neural-networks(RFNN) to approximate the nonlinear functions in a robotic manipulator system, we develop an adaptive H-infinity controller. The proposed controller can attenuate the effect of external disturbance and reduce the reconstruction-error of the recurrent fuzzy neural network to a prescribed level. Meanwhile, it also ensures all signals in the closed-loop system to be bounded. Simulation experiments of this control strategy are performed; the results show that this control strategy has better tracking-performance than the computed-torque-control method under external disturbances.