Neural sliding mode control for multi-link robots

A neural sliding mode controller is presented for trajectory tracking control of multi-link robots with uncertain external disturbances and system model errors. This approach gives a new global sliding mode manifold for the second-order multi-link robots, which enable system trajectory to run on the sliding mode manifold at the initial states and eliminate the reaching phase of conventional sliding mode control. Robustness for system dynamics is guaranteed over all the response time. A radial basis function neural network (RBFNN) is employed to eliminate chattering of global sliding mode control, and enforce the sliding mode motion by its learning the upper bound of model errors and uncertain disturbances. Moreover, the stability of the controller is proven by Lyapunov function. Simulation results verify the validity of the control scheme.