Robust Photogrammetry-Based Online Pose Correction of Industrial Robots Employing Adaptive Integral Terminal Fractional-Order Super-Twisting Algorithm

Abstract

In this paper, a novel adaptive robust control scheme is proposed for pose correction of eye-to-hand photogrammetry-based industrial robots subject to uncertainties. The proposed method uses two control loops: internal and external loops. The former is the dynamic controller designed for controlling the robot’s joints. The external loop is the kinematic controller to correct the pose error using the estimated end-effector’s pose acquired by the photogrammetry sensor (in this research C-track AMETEK). An adaptive integral terminal fractional-order super-twisting algorithm (AITFOSTA) is developed and employed for both control loops. AITFOSTA is an integral sliding mode controller (ISMC) whose nominal control law is a terminal one and its switching part is replaced with a fractional-order super-twisting algorithm (FOSTA), reducing the chattering to a great extent while rejecting the uncertainties. Additionally, an adaptive uncertainty and disturbance estimator based on radial basis function neural network (RBFNN) is designed and used as a compensator to reduce the uncertainty bounds, contributing to further chattering reduction. The stability analysis of the proposed controller is also presented. Experimental results on a PUMA200 industrial robot show superiority of the proposed method over other well-known approaches by reaching an unprecedented tracking accuracy, i.e., 0.06 mm and 0.18 deg for position and orientation, respectively.