Adaptive neural network control of an uncertain robotic manipulator with input constraint and external disturbance

This paper investigates the control problem of an uncertain robotic manipulator under the effect of bounded external disturbance and input constraint. A novel Neural Network (NN) controller is proposed to achieve asymptotic tracking of desired trajectory. The model uncertainty is lumped together with external disturbance and compensated by the NN term of the proposed adaptive controller, while the boundedness of input is ensured via an auxiliary system and a projection operator. The ISS property of the closed-loop system and the boundedness of input are rigorous proved. Finally, we show the effectiveness of the proposed controller in the numerical study.