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In this paper, a radical adaptive terminal sliding mode control method for a robotic arm with model uncertainties and external disturbances is proposed in such a way that the singularity problem is completely dealt with. A radial basis function neural network (RBFNN) with an online weight tuning algorithm is employed to approximate unknown smooth nonlinear dynamic functions caused by the fact that there is no prior knowledge of the robotic dynamic model. Furthermore, a robust control law is utilized in order to eliminate total uncertainty composed of model uncertainties, external disturbances, and the inevitable approximation errors resulting from the finite number of the hidden-layer neurons of the RBFNN. Thanks to this proposed controller, a desired performance is achieved where tracking errors converge to zero within a finite time. In accordance with Lyapunov theory, the desired performance and the stability of the whole closed loop control system are ensured to be achieved. Finally, comparative computer simulation results are illustrated to confirm the validity and efficiency of the proposed control method.

Adaptive antisingularity terminal sliding mode control for a robotic arm with model uncertainties and external disturbances