A Distributed Sensing System for Vehicles State Estimation

Abstract

This paper presents the design of a distributed sensing system that uses an Extended Kalman Filter (EKF) to fuse measurements, so that automotive vehicle states can be estimated for use by a Semi-active Suspension control system. To improve ride comfort and handling quality, relative displacements and velocities of suspension systems are estimated. To control the stability of vehicles, roll, yaw, and pitch must also be determined. The designed (EKF) uses easily accessible measurements such as accelerations and body's angular velocities. These measurements are provided by 8 accelerometers and an Inertial Measurement Unit (IMU). The accelerometers are strategically mounted on the two ends of each individual shock absorber (damper). The IMU was mounted near the vehicle's center of gravity. Computer simulations and experiments were conducted for full vehicle state estimation of a 1993 Toyota Tercel equipped with the above mentioned sensor suite. Results show that except relative displacements, all states of the automobile's semi-active suspension systems can be estimated using this set of sensors. The designed EKF works well despite not knowing accurate information about road inputs, external disturbances and car characteristics such as moments of inertia, mass, and equivalent spring and damping coefficients. Both simulation results and experimental results show the effectiveness of the designed EKF in estimating the required states.