Robust Adaptive Control for Robotic Manipulators Based on RBFNN

The rigid robotic manipulators is used in the mining industry more and more widely. An adaptive robust control algorithm of robotic manipulators based on radial basis function neural network (RBFNN) is proposed by the paper. Neural network controller is used to adaptive learn and compensate the unknown system, approach errors as disturbance are eliminated by robust controller. The weight adaptive laws on-line based on Lyapunov theory is designed. The robust controller was proposed based on H theory. Above these assured the stability of the whole system, and L2 gain also was less than the index. This control scheme possesses great control accuracy and dynamic function. The simulation results show that the presented neural network control algorithm is effective.