Whole-body vibration is an important problem facing operators of off-road vehicles. Research has shown that operators exposed to low-frequency whole-body vibration can experience temporary and even permanent injuries. One solution to this problem is to develop an active seat capable of canceling the vibrations felt by the operator. Several passive, semi-active, active, and fully active seats have been designed and built to address this problem. Furthermore, controllers have been developed to optimize the seat performance. Vibration cancellation seating systems seem to be a promising and practical way to reduce the effects of whole-body vibration.
To extend developments in this area, a Hyper-Active seat has been designed and built at the Georgia Institute of Technology. The seat utilizes a 3 Revolute-Prismatic-Revolute (RPR) parallel manipulator design which allows for the independent control of the vertical, horizontal and pitch angle directions. The seat is powered using three hydraulic actuators which are controlled using MATLAB's Simulink, xPC Target, and Real-Time Workshop. Controllers were developed to control the position of the seat, as well as cancel unwanted vibration. To test the performance of the Hyper-Active seat, the system identification of the seat was undertaken using open-loop forcing functions. The seat was evaluated in each degree of freedom to understand the potential of the seat to cancel harmful vibration.
In addition to reducing harmful vibrations, the seat can double as a safety seat to reduce injuries during a frontal collision. If the front edge of the seat bottom can be raised very quickly, then the forward motion of the passenger can be reduced during a crash. An optimal method for performing this motion is developed.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/5241 |
| Date | 12 April 2004 |
| Creators | Klooster, Samuel John |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | 3900879 bytes, application/pdf |
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