Browsing by Author "Arabi, E."
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Item Open Access Human-in-the-loop systems with inner and outer feedback control loops: adaptation, stability conditions, and performance constraints(American Institute of Aeronautics and Astronautics, 2019) Arabi, E.; Yücelen, T.; Sipahi, R.; Yıldız, YıldırayIn this paper, we focus on human-in-the-loop physical systems with inner and outer feedback control loops. Specifically, our problem formulation considers that inner loop control laws use a model reference adaptive control approach to suppress the effect of system uncertainties such that the overall physical system operates close to its ideal behavior as desired in the presence of adverse conditions due to failures and/or modeling inaccuracies. Moreover, we consider that the outer loop control laws exist owing to employing either sequential loop closure and/or high-level guidance methods. As it is true in practice, in addition, humans are considered to inject commands directly to the outer loop dynamics in response to the changes in the physical system, where the outer loop commands affect inner loop dynamics in response to the commands received from the humans as well as in response to the changes in the physical system.Item Open Access Set-theoretic model reference adaptive control for performance guarantees in human-in-the-Loop systems; a pilot study(American Institute of Aeronautics and Astronautics, Inc., 2020-01) Koru, Ahmet Taha; Doğan, K. M.; Yücelen, T.; Arabi, E.; Sipahi, R.; Yıldız, YıldırayControl 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.