RISE-Adaptive Neural Control for Robotic Manipulators With Unknown Disturbances

In this paper, a RISE-based adaptive neural network prescribed performance control is presented for the robotic manipulator with unknown disturbance. A prescribed performance function (PPF) characterizing settling time, overshoot, steady-state error, and convergence rate is presented to improve the transient performance. The unknown dynamics of the robotic manipulator are approximated by using the radial basis function neural network (RBFNN) which requires fewer adaptive parameters. The RBFNN approximation error and unknown disturbance are rejected by introducing the robust integral of the sign of the error (RISE) term. Then, an adaptive controller is designed for the robotic manipulator that can achieve precisely output tracking and guarantee the asymptotic stability of the control systems. The effectiveness of the proposed control approach is verified by simulation based on a robotic manipulator.