This test facility will enable experimental demonstrations of intelligent system identification and control algorithms, and will help attract additional government and industrial research support. These real-time algorithms will correct for tool deflections caused by cutting forces and reduce surface defects and tool damage caused by regenerative chatter. The primary research objective is to develop and experimentally demonstrate self-tuning control algorithms that account for uncertainties associated with structural vibration modes. These self-tuning algorithms, which account for parametric uncertainties and unmodeled dynamics, help improve performance by recursively identifying parameter variations of the flexible structure online. A test rig, equipped with data acquisition, is built to experimentally validate system identification and self-tuning control.
Epidemiological data indicate a strong link between LBDs and occupational sudden loads.
The specific aim of this research is to apply intelligent system identification to model the human trunk and spine dynamic parameters during sudden loads, and examine parametric changes due to the presence of preload warning signals, low back fatigue, training effects, and increased trunk flexion. Results from this research will be used to develop workplace controls that minimize detrimental muscular and kinematic responses to sudden loading. Telerobotic MIS systems use miniature grippers to provide the full mobility range of a human hand at a fraction of the size, thus eliminating the need for large incisions and resulting in shorter recovery times and reduced complications. Current work focuses on enhancing the performance of existing gripper actuation subsystems.