Model-based force estimation is an emerging methodology in the mechatronic community given the possibility to exploit physically inspired high-fidelity models in tandem with ready-to-use cheap sensors. In this work, an inverse input load identification methodology is presented combining high-fidelity multibody models with a Kalman filter-based estimator and providing the means for an accurate and computationally efficient state-input estimation strategy. A particular challenge addressed in this work is the handling of the redundant state-description encountered in common multibody model descriptions. A novel linearization framework is proposed on the time-discretized equations in order to extract the required system model matrices for the Kalman filter. The presented framework is experimentally validated on a slider-crank mechanism. The nonlinear kinematics and dynamics are well represented through a rigid multibody model with lumped flexibilities to account for localized interaction phenomena among bodies. The proposed methodology is validated estimating the input torque delivered by a driver electro-motor together with the system states and comparing the experimental data with the estimated quantities. The results show the stability and accuracy of the estimation framework by only employing the angular motor velocity, measured by the motor encoder sensor and available in most of the commercial electro-motors.

This work proposes a methodology for in situ parameter identification using system-level measurements of (flexible) multibody systems, opposed to dedicated component-level identification. The sensitivity information employed for the optimization is obtained using the adjoint variable method (AVM). This method has the advantage of obtaining sensitivity information at a computational cost independent of the amount of model parameters. The underlying flexible multibody formulation employed is a novel approach called the flexible natural coordinates formulation (FNCF). This formulation combines the advantageous properties of the floating frame of reference formulation (FFRF) and the generalized component mode synthesis (GCMS) methods and results in a constant mass and stiffness matrix with quadratic constraint equations. This work shows how the specific structure of equations obtained through FNCF drastically reduces the complexity of the AVM as the simulation derivatives can be readily obtained and are of limited order. The proposed approach has been implemented in an in-house object-oriented matlab multibody code. The methodology is illustrated by identifying 13 model parameters of a MacPherson suspension model, in situ and using system-level measurements.