State and force observers based on multibody models and the indirect Kalman filter

Abstract The aim of this work is to present two new methods to provide state observers by combining multibody simulations with indirect extended Kalman filters. One of the methods presented provides also input force estimation. The observers have been applied to two mechanism with four different sensor configurations, and compared to other multibody-based observers found in the literature to evaluate their behavior, namely, the unscented Kalman filter (UKF), and the indirect extended Kalman filter with simplified Jacobians (errorEKF). The new methods have some more computational cost than the errorEKF, but still much less than the UKF. Regarding their accuracy, both are better than the errorEKF. The method with input force estimation outperforms also the UKF, while the method without force estimation achieves results almost identical to those of the UKF. All the methods have been implemented as a reusable MATLAB® toolkit which has been released as Open Source in https://github.com/MBDS/mbde-matlab .

[1]  Agnieszka Szczęsna,et al.  Model-based extended quaternion Kalman filter to inertial orientation tracking of arbitrary kinematic chains , 2016, SpringerPlus.

[2]  Javier Cuadrado,et al.  Automotive observers based on multibody models and the extended Kalman filter , 2010 .

[3]  Alberto Trevisani,et al.  Two-stage approach to state and force estimation in rigid-link multibody systems , 2017 .

[4]  C. Loan Computing integrals involving the matrix exponential , 1978 .

[5]  Daniel Dopico,et al.  Dealing with multiple contacts in a human-in-the-loop application , 2011 .

[6]  Daniel Álvarez Mántaras,et al.  State estimation applied to non-explicit multibody models , 2016 .

[7]  Emilio Sanjurjo,et al.  Accuracy and efficiency comparison of various nonlinear Kalman filters applied to multibody models , 2017 .

[8]  Javier García de Jalón,et al.  Kinematic and Dynamic Simulation of Multibody Systems: The Real Time Challenge , 1994 .

[9]  R. Leine,et al.  A synchronization‐based state observer for impact oscillators using only collision time information , 2016 .

[10]  D. Simon Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[11]  William Cyrus Navidi,et al.  Statistics for Engineers and Scientists , 2004 .

[12]  Alberto Trevisani,et al.  State estimation using multibody models and non-linear Kalman filters , 2012 .

[13]  Emilio Sanjurjo State observers based on detailed multibody models applied to an automobile , 2016 .

[14]  Adrian Sandu,et al.  Direct and Adjoint Sensitivity Analysis of Ordinary Differential Equation Multibody Formulations , 2014, Journal of Computational and Nonlinear Dynamics.

[15]  E. J. Haug,et al.  Computer aided kinematics and dynamics of mechanical systems. Vol. 1: basic methods , 1989 .

[16]  Wim Desmet,et al.  Validation of a Real-Time Multibody Model for an X-by-Wire Vehicle Prototype Through Field Testing , 2015 .

[17]  Wim Desmet,et al.  Online state and input force estimation for multibody models employing extended Kalman filtering , 2014 .

[18]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[19]  Wim Desmet,et al.  Hard real-time multibody simulations using ARM-based embedded systems , 2016 .

[20]  J. L. Blanco-Claraco,et al.  Multibody dynamic systems as Bayesian networks: Applications to robust state estimation of mechanisms , 2014, Multibody System Dynamics.

[21]  Daniel Dopico,et al.  Real-time state observers based on multibody models and the extended Kalman filter , 2009 .

[22]  Javier Cuadrado,et al.  A comparison in terms of accuracy and efficiency between a MBS dynamic formulation with stress analysis and a non‐linear FEA code , 2001 .

[23]  Sung-Soo Kim,et al.  Real-time multibody vehicle model with bush compliance effect using quasi-static analysis for HILS , 2009 .

[24]  P. Groves Principles of GNSS, Inertial, and Multi-Sensor Integrated Navigation Systems , 2007 .

[25]  Dario Richiedei,et al.  Kinematic state estimation for rigid-link multibody systems by means of nonlinear constraint equations , 2017 .

[26]  Daniel Dopico,et al.  Determination of Holonomic and Nonholonomic Constraint Reactions in an Index-3 Augmented Lagrangian Formulation With Velocity and Acceleration Projections , 2014 .

[27]  Gaurav S. Sukhatme,et al.  Circumventing dynamic modeling: evaluation of the error-state Kalman filter applied to mobile robot localization , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[28]  Olivier A. Bauchau,et al.  Flexible multibody dynamics , 2010 .

[29]  Emilio Sanjurjo,et al.  Online Kinematic and Dynamic-State Estimation for Constrained Multibody Systems Based on IMUs , 2016, Sensors.

[30]  Daniel Dopico,et al.  Real-Time Multibody Dynamics and Applications , 2008 .

[31]  Janko Slavič,et al.  Minimization of the positional errors for an accurate determination of the kinematic parameters of a rigid-body system with miniature inertial sensors , 2014 .

[32]  D. Dopico,et al.  Penalty, Semi-Recursive and Hybrid Methods for MBS Real-Time Dynamics in the Context of Structural Integrators , 2004 .

[33]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .