The research goal of this thesis was to increase the understanding of effects of haptic feedback on human’s performance and control behavior.
Firstly, we investigated the effectiveness of haptic aids on improving human’s performance in different control scenarios.
Beneficial effects of haptic aids were shown in terms of human's performances and control effort.
Comparisons with input-mixing systems showed that, although input-mixing systems yielded better performance
than haptic aids in nominal conditions, participants recovered better from failures of haptic systems than from failures of input-mixing aids.
Secondly, we investigated how humans adapt their dynamic responses to realize benefits of the haptic feedback.
To achieve this goal, we developed novel identification methods to estimate human's neuromuscular dynamics in a multi-loop control task.
The novel methods assumed a time-invariant behavior of humans responses.
The novel methods were validated in simulation and applied to experimental data.
Finally, novel methods were developed to account for time-varying behavior of human's responses.
Different sets of numerical simulations were used to validate the novel methods.
Then, the methods were applied to data obtained in human in-the-loop experiments
