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Suspension is an important chassis part which is vital to ride comfort [1]. However, it is difficult to achieve our targeted comfortability level in a short time. Therefore, improving efficiency of damper development is our primary challenge. We have launched a project which aims to reduce the workload on developing dampers by introducing analytical approaches to the improvement of ride comfort. To be more specific, we have been putting effort into developing the damping force prediction, the vehicle dynamics prediction and subjective rating prediction. This paper describes subjective rating prediction method which output a subjective rating corresponding to the physical value of the vehicle dynamics with deep learning. As a result of verification using objective data which was not used for learning process, DNN (Deep Neural Network) prediction method could fairly precisely predict subjective rating of the expert driver.

Semi-active suspension systems have traditionally used accelerometers mounted on the wheel and body to sense vehicle motion. However, the cost and weight of these sensors and their associated bracketry and wiring must be considered when deciding to adopt a semi-active suspension system on a particular vehicle. In previous report [1], Authors have described a Bi-Linear Optimal control algorithm [2] by which sprung mass motion is estimated using height sensor signals and a Kalman filter. Such an algorithm would eliminate the need for additional accelerometers and their associated hardware, resulting in a cheaper and lighter system. In this report, the Authors propose a method of improved ride comfort and reducing tuning time of this algorithm by improving the sprung mass motion estimation method.

New ride control and handling control are developed, and installed in a system using only vehicle height sensor as dedicated sensors and pressure control type semi-active damper. Bi-linear optimal control is applied for controlling ride comfort control constructed observer which is inputted vehicle height sensor for calculating state quantity then used output of the observer. Behavior of vehicle was investigated by vehicle experiment and formalized to further improve the feeling of roll generated by handling control and devised and applied semi-active suspension control method which transiently realize the behavior. Proposed semi-active suspension system not only achieves damping performance as well as skyhook control, but also improves smooth ride comfort and handling performance including roll feeling. In this report, we describe aim, feature and effect of this system.