The quality assessment of complex systems is carried out with different criteria, such as performance behavior, energy consumption, or reliability. With a model-based design optimization, the different objectives can be included directly into the system design at an early stage. Optimization strategies can then be applied to find the best possible compromise solutions satisfying the design demands.
Not only design but also system validation can be formulated as an optimization task by formulating the design requirements as criteria. By means of optimization strategies, it is then possible to find the worst case with respect to all admissible parameter variations. The system is verified, if all criteria values remain within a specified range expressing the design demands.
The optimization tools developed in the SR institute are universally applicable and integrated in general computer-aided environments for design, modelling and simulation, such as Matlab or Modelica. Real-time variants of these methods are applied directly for online optimal control applications.