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

Kiem Nguyen,Quyen Bui,Minhtuan Pham,Tinh Nguyen

doi:10.3906/elk-1711-137

Abstract

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.

Highlights

It is evident that robotic arms have been widely applied thanks to their vital role in flexible automation processes with high speed and high accuracy
They have often been subjected to external disturbances and a number of model uncertainties, such as payload variations and dynamic parameter variations, and in practice, it is impossible to get the precise expression of the dynamics of these arms
The linear sliding mode control (SMC) method [1,2,3,4] is one of the most effective control methods to cope with the existence of the aforementioned model uncertainties as well as external disturbances and has been applied widely

Summary

Introduction

It is evident that robotic arms have been widely applied thanks to their vital role in flexible automation processes with high speed and high accuracy They have often been subjected to external disturbances and a number of model uncertainties, such as payload variations and dynamic parameter variations, and in practice, it is impossible to get the precise expression of the dynamics of these arms. As to strengthen the convergence rate, the design gains of the linear SMC method must be augmented to be bigger It may cause the harmful saturation of the control inputs. In this method, the singularity problem was prevented by switching between the terminal and linear sliding manifolds, but the results were that, still, the tracking performance could not be clearly improved owing to rough switches. Where ε is a reconstruction vector and can be made small arbitrarily

The problem statement

Computing the control inputs

Stability analysis

Conclusion