This paper describes motorized active suspension damper control with dynamic friction and actuator delay compensation for an enhanced ride quality. The control algorithm consists of a supervisory controller, an upper-level controller and a lower-level controller. The supervisory controller determines the control modes, such as the passive control modes and the active control mode. The upper-level controller, which incorporates the existing actuator delay, computes the damping force using linear quadratic control theory. The actuator input is determined by the lower-level controller by compensating the dynamic friction torque. To estimate the sprung-mass displacement, the sprung-mass velocity, the unsprung-mass displacement and the unsprung-mass velocity, two state estimators are proposed. An adaptive observer is developed for the non-linear dry friction to estimate the ball-screw dynamic friction caused by the axial movement of the actuator and the viscosity. The performance of the proposed control algorithm was evaluated from simulations. It was shown from simulations that the proposed motorized active suspension damper control with a friction and delay compensation algorithm can improve the ride quality.

This paper presents a mode control algorithm of motorized active suspension damper (MASD) for ride quality and energy efficiency. The control algorithm has been developed based on a Full-car model. The model consists of front and rear half-car dynamics. The proposed control algorithm consists of supervisory, upper-level and lower-level controllers. The control modes, such as passive and roll control modes are determined in the supervisory controller. The upper-level controller derives damping force using linear quadratic control theory. The lower level controller determines actuator input. At the same time, state estimators, vehicle body velocity estimator, suspension state estimator, are designed to estimate vehicle states used to control the actuator. The performance of the proposed mode control algorithm has been evaluated through simulations. It has been shown that the proposed MASD control algorithm improves the ride quality and energy efficiency.

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