Adaptive neural network control of a robotic manipulator with unknown backlash‐like hysteresis

摘要

This study proposes an adaptive neural network controller for a 3‐DOF robotic manipulator that is subject to backlash‐like hysteresis and friction. Two neural networks are used to approximate the dynamics and the hysteresis non‐linearity. A neural network, which utilises a radial basis function approximates the robot's dynamics. The other neural network, which employs a hyperbolic tangent activation function, is used to approximate the unknown backlash‐like hysteresis. The authors also consider two cases: full state and output feedback control. For output feedback, where system states are unknown, a high gain observer is employed to estimate the states. The proposed controllers ensure the boundedness of the control signals. Simulations are also performed to show the effectiveness of the controllers.