Bilateral telerobotic system using Type-2 fuzzy neural network based moving horizon estimation force observer for enhancement of environmental force compliance and human perception. (August 2019)

• Record Type: Journal Article
• Authors: Sun, Da; Liao, Qianfang; Stoyanov, Todor; Kiselev, Andrey; Loutfi, Amy
• Is Part Of: Automatica. Volume 106(2019)
• Page Start: 358
• Page End: 373
• Publication Date: 2019-08
• Subjects: Force estimation and control -- Type-2 fuzzy neural network -- Moving horizon estimation -- Bilateral teleoperation -- Machine vision; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2019.04.033
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: This paper firstly develops a novel force observer using Type-2 Fuzzy Neural Network (T2FNN)-based Moving Horizon Estimation (MHE) to estimate external force/torque information and simultaneously filter out the system disturbances. Then, by using the proposed force observer, a new bilateral teleoperation system is proposed that allows the slave industrial robot to be more compliant to the environment and enhances the situational awareness of the human operator by providing multi-level force feedback. Compared with existing force observer algorithms that highly rely on knowing exact mathematical models, the proposed force estimation strategy can derive more accurate external force/torque information of the robots with complex mechanism and with unknown dynamics. Applying the estimated force information, an external-force-regulated Sliding Mode Control (SMC) strategy with the support of machine vision is proposed to enhance the adaptability of the slave robot and the perception of the operator about various scenarios by virtue of the detected location of the task object. The proposed control system is validated by the experiment platform consisting of a universal robot (UR10), a haptic device and an RGB-D sensor.