Adaptive tracking control of robotic manipulators with unknown input saturation using backstepping sliding mode technique

In this paper, an adaptive tracking control scheme is proposed for robotic manipulators with unknown input saturation. To overcome the design difficulty from non-differential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function, which can be further transformed into an affine form according to the mean-value theorem. Then, a simple sigmoid neural network is employed to approximate the uncertain parts including saturation in the system. By combing the backstepping technique and the sliding-mode control, virtual controls are designed in each step. With the proposed scheme, no prior knowledge is required on the bound of input saturation, and comparative simulations are given to illustrate the effectiveness of the proposed method.