Analysis of Human Remnant in Pursuit and Preview Tracking Tasks

Abstract

In manual pursuit and preview tracking tasks, humans apply feedforward control to exploit available information of the target trajectory to follow. While the human's linear, time-invariant dynamics in such tasks are well-understood and have been modeled in the quasi-linear framework, the remaining nonlinear and time-invariant control behavior, the human remnant, is typically ignored. This paper extends the current state-of-the-art theories of human remnant, which are applicable to compensatory tracking tasks only, to the more common and relevant pursuit and preview tracking tasks. Data are presented from three human-in-the-loop tracking experiments. The ratio of the remnant relative to the linear control output is quantified in the frequency domain, and remnant spectra are computed and modeled. The results show that the injected remnant is identical in compensatory, pursuit, and preview tasks, regardless of the task's controlled element dynamics, preview time, and target trajectory bandwidth. The presented remnant data and models can be used together with already available linear, time-invariant models, to better predict characteristics of human control behavior in pursuit and preview tracking tasks, enabling the design of human assistance systems.