## 4 Discussion and Concluding Remarks  
Modelling and control of flexible mechatronic systems is a nontrivial task that is difficult to automate, even with the aid of all the available computer software tools. Deep insight and understanding of the problem is still fundamental for all the phases of machine design. We tried to gather best practices currently available in the fields of analytical modelling, finite element analysis, system identification, and robust control and applied them to our benchmark problem. Our observations can be boiled into a few concluding remarks:  
* **Even simple systems such as our flexible manipulator benchmark exhibit complex dynamic behaviour** when mechanical compliance comes into play and the resonance frequencies of the bending modes overlap with target closed-loop bandwidth. Care must be taken in all the steps of design, modelling, identification, and control. * **Analytical modelling methods can provide valuable insight** to general properties of flexible systems. However, explosion of complexity in the case of nontrivial geometric and material properties may be the main limitation for their practical utilisation. * **Finite element analysis proved to be an invaluable modelling tool** capable of delivering high-fidelity models based on the machine geometry. Still, there are remaining open issues when using FEM models for a control design purpose. The linearisation and reduction steps that are necessary to produce useful control-relevant models require careful choices in terms of balancing the fidelity and complexity of the outcomes. Dynamic transient simulations with full-scale nonlinear FEM models require an excessive amount of computations. The user then has to weigh potential benefits in comparison with simple linear simulations. Intermediate simplified nonlinear models may be needed to deliver expected results in reasonable time. Our benchmark prob  
Figure 27: NL deflection of the blade for 700% of maximum actuator torque disturbance level. lem has shown that even well-developed linear reduced-order models can provide high-fidelity predictions of both open- and closed-loop machine behaviour. * **It was demonstrated that the use of the full 3D nonlinear simulation was not mandatory** for the current application, and the ROM proved to be sufficient for the purpose of control design and closed-loop performance predictions. Considering the applicable level of solicitations, the flexible arm of the manipulator is not submitted to significant NL vibrations. Nevertheless, this conclusion will not be valid for different operating conditions far from the assumed point of linearisation and for other mechatronic systems with very low stiffness. * **System identification methods offer powerful algorithms capable of extracting information from experimental data**. Especially in the linear domain, many highly capable and practice-proven methods are readily available. Experimental observations can support both the modelling and control design phases of the machine development cycle. Machine geometry, stiffness, and damping can be tuned in the analytical or FEM models based on the experiments to improve their predictive capability, as shown in our use case. On the other hand, models from data can be often directly used for the purpose of control design. The two realms of first-principle modelling and data-driven identification can be connected to benefit from both prior information and experimental observations. This approach is expected to be accented more in a near future with the arrival of the Digital Twin concepts requiring the models to be continuously updated with experimental data and live parallel lives with their physical counterparts. * **The FEA is very useful for extrapolating its predictions in case of machine design changes**. The expected dynamic behaviour can be estimated without the necessity of construction and assembly of numerous prototypes to gather experimental data. This was demonstrated on our variable load-mass position scenario.