Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/6382
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dc.contributor.authorHussain, Irfanen_US
dc.contributor.authorAlbalasie, Ahmaden_US
dc.contributor.authorAwad, Mohammeden_US
dc.contributor.authorSeneviratne, Lakmalen_US
dc.contributor.authorGan, Dongmingen_US
dc.date.accessioned2020-05-09T09:15:29Z-
dc.date.available2020-05-09T09:15:29Z-
dc.date.issued2018-01-11-
dc.identifier.urihttp://hdl.handle.net/20.500.11889/6382-
dc.descriptionArticle published in : Frontiers Robotics AI, June 2018, vol. 5, article 68en_US
dc.description.abstractThis research work aims at realizing a new compliant robotic actuator for safe human-robotic interaction. In this paper, we present the modeling, control, and numerical simulations of a novel Binary-Controlled Variable Stiffness Actuator (BcVSA) aiming to be used for the development of a novel compliant robotic manipulator. BcVSA is the proof of concept of the active revolute joint with the variable recruitment of series-parallel elastic elements. We briefly recall the basic design principle which is based on a stiffness varying mechanism consisting of a motor, three inline clutches, and three torsional springs with stiffness values (K0, 2K0, 4K0) connected to the load shaft and the motor shaft through two planetary sun gear trains with ratios (4:1, 4:1 respectively). We present the design concept, stiffness and dynamic modeling, and control of our BcVSA. We implemented three kinds of Multiple Model Predictive Control (MPC) to control our actuator. The main motivation of choosing this controller lies in the fact that working principle of multiple MPC and multiple states space representation (stiffness level) of our actuator share similar interests. In particular, we implemented Multiple MPC, Multiple Explicit MPC, and Approximated Multiple Explicit MPC. Numerical simulations are performed in order to evaluate their effectiveness for the future experiments on the prototype of our actuator. The simulation results showed that the Multiple MPC, and the Multiple Explicit MPC have similar results from the robustness point of view. On the other hand, the robustness performance of Approximated Multiple Explicit MPC is not good as compared to other controllers but it works in the offline framework while having the capability to compute the sub-optimal results. We also performed the comparison of MPC based controllers with the Computed Torque Control (CTC), and Linear Quadratic Regulator (LQR). In future, we are planning to test the presented approach on the hardware prototype of our actuator.en_US
dc.language.isoenen_US
dc.publisherFrontiersen_US
dc.relation.ispartofFrontiers Robotics AIen_US
dc.subjectActuators - Automatic controlen_US
dc.subjectPredictive controlen_US
dc.subjectRobotics - Human factorsen_US
dc.subjectRobots - Control systemsen_US
dc.titleModeling, control, and numerical simulations of a novel binary-controlled variable stiffness actuator (BcVSA)en_US
dc.typeArticleen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.item-access-typeopen_accessen_US
newfileds.thesis-prognoneen_US
newfileds.general-subjectEngineering and Technology | الهندسة والتكنولوجياen_US
dc.identifier.doihttps://api.elsevier.com/content/abstract/scopus_id/85050157077-
dc.identifier.doihttps://api.elsevier.com/content/abstract/scopus_id/85050157077-
dc.identifier.doihttps://api.elsevier.com/content/abstract/scopus_id/85050157077-
dc.identifier.doihttps://api.elsevier.com/content/abstract/scopus_id/85050157077-
dc.identifier.doihttps://api.elsevier.com/content/abstract/scopus_id/85050157077-
dc.identifier.doihttps://api.elsevier.com/content/abstract/scopus_id/85050157077-
dc.identifier.doihttps://api.elsevier.com/content/abstract/scopus_id/85050157077-
dc.identifier.doi10.3389/frobt.2018.00068-
dc.identifier.doihttps://api.elsevier.com/content/abstract/scopus_id/85050157077-
dc.identifier.doihttps://api.elsevier.com/content/abstract/scopus_id/85050157077-
dc.identifier.doihttps://api.elsevier.com/content/abstract/scopus_id/85050157077-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85050157077-
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