Adaptive Neural Network-Based Finite-Time Impedance Control of Constrained Robotic Manipulators With Disturbance Observer

This brief proposes an adaptive neural network-based finite-time impedance control method for constrained robotic manipulators with disturbance observer. Firstly, by combining barrier Lyapunov functions with the finite-time stability control theory, the control system has a faster convergence rate without violating the full state constraints. Secondly, the adaptive neural network is introduced to approximate the unmodeled dynamics and a disturbance observer is designed to compensate for the unknown time-varying disturbances. Then, the command filtered control technique with error compensation mechanism is used to deal with the “explosion of complexity” of traditional backstepping and improve the control accuracy. The simulation results show the effectiveness of the proposed control method.