The field of mechatronic systems is moving towards the development of more efficient and accurate methods for model reduction and control. Researchers are focusing on creating innovative techniques that can handle complex systems with multiple inputs and outputs, while also ensuring computational efficiency and stability. One of the key directions is the development of parametric reduced order models that can accurately predict the behavior of systems over a wide range of parameters. Another area of research is the improvement of model predictive control methods, including the development of new algorithms and techniques for optimizing performance and reducing computational overhead. Noteworthy papers in this area include:

• Parametric Model Order Reduction by Box Clustering with Applications in Mechatronic Systems, which introduces a new method for parametric model reduction that can handle large parameter ranges without sacrificing computational efficiency.
• Efficient Configuration-Constrained Tube MPC via Variables Restriction and Template Selection, which proposes a new framework for model predictive control that can balance polytope complexity and conservatism. These developments have the potential to significantly impact the design and optimization of complex systems, enabling the creation of more efficient and reliable mechatronic components.