To comply with the increasing complexity of new mechatronic systems and stricter safety regulations, advanced estimation algorithms are currently undergoing a transformation towards higher model complexity. However, more complex models often face issues regarding the observability and computational effort needed. Moreover, sensor selection is often still conducted pragmatically based on experience and convenience, whereas a more cost-effective approach would be to evaluate the sensor performance based on its effective estimation performance. In this work, a novel estimation and sensor selection approach is presented that is able to stabilise the estimator Riccati equation for unobservable and non-linear system models. This is possible when estimators only target some specific quantities of interest that do not necessarily depend on all system states. An Extended Kalman Filter-based estimation framework is proposed where the Riccati equation is projected onto an observable subspace based on a Singular Value Decomposition (SVD) of the Kalman observability matrix. Furthermore, a sensor selection methodology is proposed, which ranks the possible sensors according to their estimation performance, as evaluated by the error covariance of the quantities of interest. This allows evaluating the performance of a sensor set without the need for costly test campaigns. Finally, the proposed methods are evaluated on a numerical example, as well as an automotive experimental validation case.

This paper presents a novel system-level model order reduction scheme for flexible multibody simulation, namely the system-level affine projection (SLAP). Contrary to existing system-level model order reduction approaches for multibody systems simulation, this methodology allows to obtain a constant reduced order basis which can be obtained in a noninvasive fashion with respect to the original flexible multibody model. It is shown that this scheme enables an automatic joint constraint elimination which can be obtained at low computational cost through exploitation of the component level modes typically employed in flexible multibody simulation. The equations of motion are derived such that the computational cost of the resulting SLAP model is independent of the original model size. This approach results in a set of ordinary differential equations with a constant mass matrix and nonlinear internal forces. This structure makes the resulting model suitable for a range of estimation, control, and design applications. The proposed approach is validated numerically on a flexible four-bar mechanism and shows good accuracy for a very low-order SLAP model. K E Y W O R D S flexible multibody, model reduction, nonlinear INTRODUCTIONOver the past decades flexible multibody simulation has demonstrated itself as a powerful framework for the dynamic analysis of mechanical systems consisting of multiple components. For many industrial applications, the small deformation assumption in particular has led to the development of range of efficient descriptions like the floating-frame-of-reference component mode synthesis (FFR-CMS) 1,2 and generalized component mode synthesis approaches (GCMS), 3,4 and more recently the flexible natural coordinate formulation (FNCF). 5 The possibility of these methods for effectively describing the system-level dynamics at a feasible computational cost, in contrast to more general nonlinear finite element approaches, has led to an increasing interest in exploiting (flexible) multibody simulation paradigms in a range of novel frameworks like:• model-based state-estimation 6-8 and• model-based control and design. 9However, the broad application of these general frameworks for multibody system models faces two main difficulties:Int J Numer Methods Eng. 2020;121:3083-3107. wileyonlinelibrary.com/journal/nme

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