Robust Control for Robot Manipulators: Support Vector Regression Based Command Filtered Adaptive Backstepping Approach

Abstract

Abstract This study derives a robust adaptive control for electrically driven robot manipulators using support vector regression (SVR) based command filtered adaptive backstepping approach. The robot system is supposed to be subject to model uncertainties, neglected dynamics, and external disturbances. Command filtered backstepping algorithm is extended to the case of robot manipulators. A robust term is added to the common adaptive support vector regression algorithm, to mitigate the effects of SVR approximation error on the path tracking performance. The stability analysis of the closed loop system using the Lyapunov theory permits to highlight adaptation laws and to prove that all signals of the closed loop system are bounded. Simulations show the effectiveness of the proposed control strategy.