Automotive observers based on multibody models and the extended Kalman filter

Abstract

This work is part of a project aimed to develop automotive real-time observers based on detailed multibody models and the extended Kalman filter (EKF). In previous works, a four-bar mechanism was studied to get insight into the problem. Regarding the formulation of the equations of motion, it was concluded that the state-space reduction method known as matrix-R is the most suitable one for this application. Regarding the sensors, it was shown that better stability, accuracy and efficiency are obtained as the sensored magnitude is a lower derivative and when it is a generalized coordinate of the problem. In the present work, the automotive problem has been already addressed, through the selection of a Volkswagen Passat as a case-study. A model of the car containing fourteen degrees of freedom has been developed. The observer algorithm that combines the equations of motion and the integrator has been reformulated so that duplication of the problem size is avoided, in order to improve efficiency. A maneuver of acceleration from rest and double lane change has been defined, and tests have been run for the "prototype", the "model" and the "observer", all the three computational, with the model having 100 kg more than the prototype. Results have shown that good convergence is obtained, but the computational cost is high, still far from real-time performance.

Patient comfort level prediction during transport using artificial neural network

“…An accelerometer is a device that can detect and measure static gravity influence, as well as dynamic movement influence. The authors of [17][18][19] devised automotive real-time observers and an attitude estimation system based on an extended Kalman filter (EKF). The authors of [20] detected road potholes with high-pass filtered accelerometer data.…”

Online Kinematic and Dynamic-State Estimation for Constrained Multibody Systems Based on IMUs

“…Among the most theoretically shifted works, we find the development of new formulations [3,4,5], the study of how to introduce flexible bodies and joints [6,7], the analysis of contacts and impacts [8,9] or efficiency improvements [10,11,12]. Other works have focused on practical applications such as vehicle dynamics [13,14,15], robotics [16,17] or biomechanics [18,19]. Some of these works incorporate experiments with real prototypes, aimed at verifying assumptions, evaluating the uncertainty of models, analyzing the influence of tuning certain parameters or even developing new applications.…”

“…The simultaneous resolution of the dynamics and constraint equations has its own techniques (see the references [3,5,6]). Among the papers found in the literature dealing with state observers under this framework (on which the present paper focuses), only those two studies, [14,27], include experiments based on real systems. The former considers position-level data collected by real sensors, whereas the latter uses simulated signals.…”

An FEM-Based State Estimation Approach to Nonlinear Hybrid Positioning Systems

“…Subsequently, 2 Mathematical Problems in Engineering a robust Extended Kalman Filtering modeled in [9] shows that it outperforms EKF and EKF2 in cases where there is blunder measurement or considerable linearization errors present only in simulation cases. Although EKF is popular in many navigation applications [10][11][12][13][14][15], it must satisfy three assumptions for using EKF and its variants. (1) The deviations of the reference state trajectory should be small.…”