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The ride values of passenger cars are investigated for Korean subjects based on the vibration of the human bodies. When three subjects are excited by driving a vehicle on road, their responses of acceleration are measured at 12 points on their bodies according to Griffin's 12 axis system (3 translational axes on a seat surface, 3 rotational axes on a seat surface, 3 translational axes at the seat back and the 3 translational axes at the feet). Since one of the most important parameters for ride comfort is the level and duration of the root mean square acceleration experienced, the ride values, such as the seat effective amplitude transmissibility, the component ride value, and the overall ride value based on acceleration root mean square are evaluated for different four vehicles using frequency weighing functions and axis multiplying factors. The ride indices are also studied considering to the seat dynamic characteristics with subjects.

The ride values of seven cars(six passenger car and one RV car) are evaluated for 4 subjects based on the vibration of the bodies. And the seat qualities are investigated with the SEAT(seat effective amplitude transmissibility) value. The evaluated values are arranged as DB in html files. Since one of the most important parameters for ride comfort is the level and duration of the root mean square acceleration experienced, the acceleration responses of subjects are measured at 8 points on their bodies(3 Translational axes on the seat surface, 3 translational axes at the feet and 2 axes(x,z) at the backrest) when the subjects are excited by driving a vehicle on the road. The ride values such as the overall ride value, the component ride values and the seat effective amplitude transmissibility based on acceleration root mean square are evaluated for different seven vehicles using frequency weighting functions and axis multiplying factors.

The objective of this study is to develop a neuro controlled active suspension for the ride quality improvement. The performance index of the optimal control is represented as the frequency-shaped using Parseval's theorem. The incorporation of frequency-dependent weighting matrices allow one to emphasize the specific variables related to the vibrations of the specific bands of frequencies. Once the active control law is obtained, we use the artificial neural networks to train the neuro controller to learn the relation of road input and control force. From the numerical results, we found that back propagation learning does good pattern matching and the neuro controlled suspension may reduce the vertical acceleration of the driver's seat and sprung mass motions significantly at desired bands of frequencies.