Set-theoretic model reference adaptive control for performance guarantees in human-in-the-Loop systems; a pilot study

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buir.contributor.authorKoru, Ahmet Taha
buir.contributor.authorYıldız, Yıldıray
dc.citation.epage12en_US
dc.citation.spage1en_US
dc.contributor.authorKoru, Ahmet Taha
dc.contributor.authorDoğan, K. M.
dc.contributor.authorYücelen, T.
dc.contributor.authorArabi, E.
dc.contributor.authorSipahi, R.
dc.contributor.authorYıldız, Yıldıray
dc.coverage.spatialOrlando, FLen_US
dc.date.accessioned2021-01-28T06:30:42Z
dc.date.available2021-01-28T06:30:42Z
dc.date.issued2020-01
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentDepartment of Mechanical Engineeringen_US
dc.descriptionDate of Conference: 6-10 January 2020en_US
dc.descriptionConference name: AIAA SciTech 2020 Forum - Advances in Adaptive Control for Aerospace Systems IVen_US
dc.description.abstractControl design that achieves high performance in human-in-the-loop machine systems still remains a challenge. Model reference adaptive control (MRAC) is well positioned for this need since it can help address issues of nonlinearities and uncertainties in the machine system. Moreover, given that human behavior is also nonlinear, task-dependent, and timevarying in nature, MRAC could also offer solutions for a highly synergistic human-machine interactions. Recent results on set-theoretic MRAC further our understanding in terms of designing controllers that can bring the behavior of nonlinear machine dynamics within a tolerance of the behavior of a reference model; that is, such controllers can make nonlinear and uncertain dynamics behave like a “nominal model.” The advantage of this argument is that humans can be trained only with nominal models, without overwhelming them with extensive training on complex, nonlinear dynamics. Even only with a training on simple nominal models, human commands when supplemented with set-theoretic MRAC can help control complex, nonlinear dynamics. In this study, we present a computer-based simulator that our research team tested under various conditions, as preliminary results supporting the promise of a simpler yet more effective means to train humans and to still achieve satisfactory performance in human-machine systems where humans are presented with complex, nonlinear dynamics.en_US
dc.identifier.doi10.2514/6.2020-1340en_US
dc.identifier.urihttp://hdl.handle.net/11693/54935
dc.language.isoEnglishen_US
dc.publisherAmerican Institute of Aeronautics and Astronautics, Inc.en_US
dc.relation.isversionofhttps://doi.org/10.2514/6.2020-1340en_US
dc.titleSet-theoretic model reference adaptive control for performance guarantees in human-in-the-Loop systems; a pilot studyen_US
dc.typeConference Paperen_US

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