State estimation and damping control for unmanned ground vehicles with semi-active suspension system

Abstract

The new type of transportation based on intelligent driverless vehicles will bring great changes to people’s travel modes and put forward higher requirements for the ride comfortability of vehicles. This paper presents a new observing algorithm to estimate the suspension states in real time and cooperate with sliding mode controller to improve the ride comfortability. First, the nonlinear model of an air suspension system equipped with a continuously controllable damper is described in detail. Then, this nonlinear suspension model is linearized precisely based on the differential geometry theory; a linear Kalman filter observer is implemented for this linearized model; through the coordinate reverse transformation, the designed linear observer is transformed into a nonlinear one, which will be suitable for the original nonlinear system, so that, the proposed state observer can estimate all the states of the nonlinear quarter car suspension system. Then, in this nonlinear suspension system, a model reference sliding mode controller is designed to continuously control the damping force to improve the ride comfortability. Finally, the effectiveness and advantage of the proposed feedback linearization Kalman observer is illustrated by comparing with a traditional extended Kalman filter observer. The simulation research shows that the proposed feedback linearization observer enjoys a better estimation accuracy, higher operation efficiency, and greater control performance while cooperating with the sliding mode controller in ride comfortability control.