Self-organizing Neural Sliding Mode Control for Multi-link Robots

A self-organizing neural sliding mode controller (SONSMC) is presented for trajectory tracking control of multi-link robots with model errors and uncertain disturbances. This approach gives a new global sliding mode manifold for multi-link robots, which enable system trajectory to run on the sliding mode manifold at the start point and eliminate the reaching phase of the conventional sliding mode control. Robustness for system dynamics is guaranteed over all the response time. A self-organizing neural network (SONN) 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. SONN can optimize its structure according to the controlled system real-time accuracy. Therefore, the controlled system accuracy is improved. The control laws are calculated by Lyapunov stability method, which ensure that the controlled system is stable. Simulation results verify the validity of the control scheme.