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This thesis explores the imperative of intelligent data and potential analysis in the realm of mechatronic product development. The persistent challenges of synchronization and efficiency underscore the need for advanced methodologies. Leveraging the substantial advancements in Artificial Intelligence (AI), particularly in generative AI, presents unprecedented opportunities. However, significant challenges, especially regarding robustness and trustworthiness, remain unaddressed.

In response to this critical need, a comprehensive methodology is introduced, examining the entire development process through the illustrative V-Model and striving to establish a robust AI landscape. The methodology explores acquiring suitable and efficient knowledge, along with methodical implementation, addressing diverse requirements for accuracy at various stages of development. 

As the landscape of mechatronic product development evolves, integrating intelligent data and harnessing the power of AI not only addresses current challenges but also positions organizations for greater innovation and competitiveness in the dynamic market landscape.

This thesis explores the potential of Artificial Intelligence (AI) in mechatronic product development, focusing on the identification, utilization, and evaluation of AI-driven approaches. The increasing complexity of cross-domain collaboration, coupled with the demand for efficiency and reliability, necessitates structured methodologies to systematically integrate AI into engineering processes. While AI offers significant opportunities, challenges related to trustworthiness, robustness, and effective implementation remain critical considerations.

To address these challenges, this work introduces a generalized five-step methodology, providing a structured framework for assessing AI’s role in mechatronic development. The methodology enables the targeted identification of AI potential, structured integration into engineering workflows, and systematic evaluation of its impact. By applying this framework to real-world industrial case studies, the thesis demonstrates its practical applicability across different AI use cases, including translation, interpretation, and prediction.

As mechatronic product development continues to evolve, leveraging AI in a structured and validated manner ensures that organizations not only overcome current challenges but also enhance innovation, decision-making, and cross-domain collaboration. The findings of this thesis provide a scalable foundation for AI-driven advancements while maintaining a balance between AI potential and investment considerations.

The automotive industry is experiencing a significant transformation driven by the demand for automation, autonomy and resource reduction. A key factor in this transformation is the model-based design and validation of advanced vehicle systems, particularly Steer-by-Wire systems, which are essential for highly automated and autonomous vehicles. However, Steer-by-Wire systems, characterized by the absence of a mechanical connection between the steering wheel and the front wheels, present unique challenges for achieving robust control as well as ensuring driving comfort and safety. This dissertation addresses these challenges by exploring innovative approaches for the optimal control of Steer-by-Wire systems, highlighting the model-based design and the integration of simulation environments. For this, a detailed model is developed, considering all relevant degrees of freedom and nonlinear characteristics of a real Steer-by-Wire system. Based on this detailed model, the dissertation presents a novel multivariable control approach that enhances the robustness and performance of Steer-by-Wire systems compared to traditional designs. The derived control approach demonstrates improved system stability and performance, effectively addressing parameter uncertainties and varying driving conditions. These satisfactory characteristics are validated both in an augmented simulation environment and on a real prototype. By combining virtual testing within the augmented simulation environment with real-world prototyping, the need for labor-intensive physical testing is minimized, thus optimizing development resources and time. The presented methods are not only employed for the development of Steer-by-Wire systems, but also for further applications in automotive engineering, including driver assistance systems, sensor evaluations and perception systems. In conclusion, the research contributes to mechatronics and automotive engineering by advancing autonomous driving through robust control approaches, virtual testing and agile development strategies. The insights and methodologies proposed not only advance the development of novel Steer-by-Wire systems, but can also serve as a basis for future innovations in mechatronic systems that require precise control and reliability.