Adaptive Neural Control of Robotic Manipulators with Adaptive Disturbance Compensation

This paper presents an adaptive neural network control approach for robotic manipulators to enhance trajectory tracking accuracy in the face of unknown system dynamics and external disturbances. The proposed method designs an adaptive compensation term to deal with the total disturbance from neural network approximation errors and unknown bounded disturbances without requiring prior knowledge of their magnitudes. The proposed scheme is validated through Lyapunov stability theory, ensuring the uniform ultimate boundedness of the system and the high-performance tracking control. Simulation results demonstrate the effectiveness of the proposed control scheme in achieving robust tracking performance under bounded unknown disturbances.