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.

Development of a systematic and practical methodology for the design of vehicles semi-active suspension control system

“…In the next step, EKF/filters are developed [12] to fuse the measurements of the sensors and estimate the vehicle states required by the semi-active controller.…”

“…The vehicle vertical model is first derived [12] and verified through 4-poster tests, as described in Section 2. The test vehicle is also instrumented with a full distributed sensor configuration consisting of eight accelerometers, one inertial measurement unit (IMU) and four displacement sensors.…”

“…The sensors data are assumed to be corrupted by a zero-mean white noise v with a covariance of R. By employing a proper sensor configuration, most of the vehicle's required states can be observed through the EKF [12]. In this case, the EKF simply appears as a low-pass filter whose bandwidth is a function of the Q and R matrices.…”

“…As described in Section 2, an EKF/filters are separately designed [12] (with the proper bandwidth) which provides the vehicle states. Theorem 1 confirms that the integrated estimator and controller loop of Figure 7, which is described by the following equation:…”

“…The figure demonstrates that the resonance frequencies of the body must be around 2 Hz. The vehicle model, in this case, is a linear 7 degree of freedom (DOF) state-space formulation [12] whose parameters have been revised after various experimental tests on the 4-posters rig. The eigenvalues of the state matrix (with minimum semi-active damping coefficients) are repetitively located at (−0.3 ± j 10.5) and (−0.8 ± j 58).…”