A Discrete-Time Extended Kalman Filter Approach Tailored for Multibody Models: State-Input Estimation

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.

Automated summary

Model-based force estimation utilizing high-fidelity multibody models and a Kalman filter-based estimator is presented for accurate input load identification. The methodology is experimentally validated on a slider-crank mechanism, showing stability and accuracy in estimating input torque and system states. The framework efficiently handles redundant state descriptions and nonlinear dynamics, demonstrating the potential for practical applications in mechatronics.