| Humans learn to interact with many complex dynamic systems such as helicopters, bicycles, and automobiles. This dissertation develops a subsystem identification method to model the control strategies that human subjects use in experiments where they interact with dynamic systems. This work provides new results on the control strategies that humans learn.;We present a novel subsystem identification algorithm, which can identify unknown linear time-invariant feedback and feedforward subsystems interconnected with a known linear time-invariant subsystem. These subsystem identification algorithms are analyzed in the cases of noiseless and noisy data.;We present results from human-in-the-loop experiments, where human subjects interact with a dynamic system multiple times over several days. Each subject's control behavior is assumed to have feedforward (or anticipatory) and feedback (or reactive) components, and is modeled using experimental data and the new subsystem identification algorithms. The best-fit models of the subjects' behavior suggest that humans learn to control dynamic systems by approximating the inverse of the dynamic system in feedforward. This observation supports the internal model hypothesis in neuroscience. We also examine the impact of system zeros on a human's ability to control a dynamic system, and on the control strategies that humans employ.;KEYWORDS: Human Motor Control, Human Learning, Human-In-The-Loop, Subsystem Identification. |
