## 1 Introduction
Formerly a static abstraction of the reality used in the early stages of the development cycle is being gradually transformed into a dynamic object living a parallel life with the physical plant and continuously updating its inner states, structure, or parameters. The goal is to improve the fidelity of the model and obtain the best achievable digital copy of the real system. This allows enhancing the predictive quality of the model to bring new possibilities in terms of monitoring, predictive or reactive maintenance and diagnostics, and accurate model-based control design. The Digital Twin can be instanced, representing a particular piece of hardware to capture specific deviations from the idealised expected behaviour, e.g., due to production variations or increasing wear during machine operation. It can be involved in all stages of a product or system's life cycle, including the design, the production, and the operation. The key difference between Digital Twin modelling and conventional numerical models is that besides the design of the product or system, the Digital Twin model incorporates all applicable data for each instance of the product or system separately. Among others, the Digital Twin model can include data considering the production, tests and measurements, operation history, and sensor data in its predictions and adapts itself continuously by synchronisation with the latest available data. Furthermore, physics-based modelling techniques such as the finite element method (FEM) can be incorporated in a Digital Twin, by continuously synchronising history and model parameters with measurement, sensor, and history data of an instance of the product. Finite element method (FEM) belongs to the class of widely applied physics-based modelling approaches. Using the FEM models for studying and analysing underlying phenomena is often designated as Finite element analysis (FEA). Many software packages that implement the FEM are available, both commercial (Abaqus, OOFELIE, Ansys and Nastran for example) and free (Elmer, Code-Aster and CalculiX for example). Books about the theory of the FEM include [8; 9]. The advantage of an FE model is that the entire mechanism response is available, not just the few lumped parameters. This enables the use of the model as a Digital Twin. It may be used to optimise sensor placement, detect damage, predict fatigue, schedule maintenance, gather data to improve redesign, and so on. However, having a high fidelity FE model is not enough. The model is not useful as a Digital Twin if evaluating it costs hours or even days. It must be much faster. This is where model reduction comes in. This paper presents an example of how a FE model can be reduced. This example is related to mechatronic design, but it could be expanded to cover more Digital Twin functionalities. The main motivation of our research was to review state-of-the-art techniques, methods, algorithms, and software tools available in the field of modelling, identification, and control and connect them in a suitable workflow applicable for designing high-fidelity motion systems. While there are many research papers dealing with the individual parts of the design process separately, we felt that a unifying view allowing us to bridge different fields of mechatronics will be beneficial for researchers and engineers working in the respective domains. Special attention is paid to the derivation of control-relevant models applicable to generic robust control methods. Subsequent steps of physical modelling using both analytical and FEM approaches, model linearization, order reduction, and model-based controller synthesis are demonstrated on a chosen benchmark problem dealing with flexible manipulator arm control. The predictive capability of the developed models is analysed by comparing their outputs with the experimentally acquired data. Suggestions on how to improve model fidelity based on the experiments are given.