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

Control 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.