Design of Parameter Adaptive Suspension Controllers with Kalman Filter for Ride Comfort Enhancement and Motion Sickness Reduction

Featured Application: Parameter adaptive controller design for ride comfort improvement and motion sickness reduction in electric vehicles with autonomous driving function. This paper presents a method to design a parameter adaptive suspension controller to boost ride comfort and to reduce motion sickness. According to recently published papers, combined motions of a sprung mass (SPMS) along heave and pitch directions tend to make motion sickness severe. To reduce motion sickness, it is necessary to design a controller which can reduce the heave and pitch vibrations of a SPMS. To avoid full-state feedback which is very difficult to implement in a real vehicle, a static output feedback (SOF) control is chosen as a feedback structure. With the SOF structure, linear quadratic SOF and parameter adaptive controllers are designed. When designing parameter adaptive controllers, an extended Kalman filter (EKF), equivalent to recursive least square (RLS), is selected for parameter adaptation. To verify performance of the controllers, simulation is performed on vehicle simulation tool. From simulation responses, it is checked whether the proposed parameter adaptive controllers are effective or not and which is the best controller, with respect to ride comfort and motion sickness.