Adaptive Backstepping Neural Tracking Control of an Uncertain Robot Manipulator with Dynamic Disturbances

Abstract

The varying system parameters, end effector payload and environmental uncertainties are quite natural in real-world robotics applications. Therefore in order to adapt to the changing control environment and improving robustness of the controller due to an uncertain system model and dynamic uncertainties adaptive control methods are developed. This paper presents an adaptive neural backstepping control for an uncertain robot manipulator with dynamic disturbances. The dynamics of an n-link uncertain robot manipulator with dynamic disturbances is expressed as a class of 3n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> order nonlinear multi-input multioutput (MIMO) system using a nonlinear disturbance observer-based model. The uncertain plant and disturbances dynamics are approximated using Radial Basis Function Network (RBFN) to derive the control law. The proposed controller for each link has a simple structure with a single unknown parameter. The update law for this unknown parameter has been obtained using Lyapunov stability. It is shown that the proposed controller is able to ensure the semi-global uniformly ultimately boundedness (UUB) of all signals of the resulting closed-loop system and the actual response eventually reaches a bounded neighbourhood of the desired response. Simulation results demonstrate the feasibility of the proposed technique. The tracking performance of the proposed controller is validated experimentally on a four degrees-of-freedom (4 DOF) Barrett Whole Arm Manipulator while performing dynamic ball hitting experiments.