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Bilateral telerobotic system using Type-2 fuzzy neural network based moving horizon estimation force observer for enhancement of environmental force compliance and human perception
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-0334-2554
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0001-8119-0843
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-6013-4874
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-0305-3728
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2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 106, p. 358-373Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Pergamon Press, 2019. Vol. 106, p. 358-373
Keywords [en]
Force estimation and control, Type-2 fuzzy neural network, Moving horizon estimation, Bilateral teleoperation, Machine vision
National Category
Control Engineering
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-74377DOI: 10.1016/j.automatica.2019.04.033ISI: 000473380000041Scopus ID: 2-s2.0-85065901728OAI: oai:DiVA.org:oru-74377DiVA, id: diva2:1317799
Funder
Swedish Research CouncilAvailable from: 2019-05-23 Created: 2019-05-23 Last updated: 2019-11-13Bibliographically approved

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Sun, DaLiao, QianfangStoyanov, TodorKiselev, AndreyLoutfi, Amy

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