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The paper shows a methodology for designing a complex system in a collaborative design environment by sharing information on autonomous subsystems and coupling parameters. Collaborative design is a current issue in aviation industry where intellectual property rights of individual companies prohibit a common evaluation model. State of the art approaches based on nested local optimisation loops are not sufficient if time-consuming component analyses are involved. A new approach for collaborative design is developed where the overall optimisation of a complex system is split into subsystem optimisation problems. Coupling parameters are treated as local design variables and only optimal coupling parameters are exchanged regularly. Application to a test example shows a huge time reduction and improved probability for finding the global optimum. Advantages are also demonstrated for an industrial design task.
Simulation studies on an active all-wheel-steering car show that disturbance of vehicle parameters have high influence on lateral car dynamics. This motivates the need of robust design against such parameter uncertainties. A specific parametrisation is established combining deterministic, velocity dependent steering control parameters with partly uncertain, velocity independent vehicle parameters for simultaneous use in a numerical optimisation process. Model-based objectives are formulated and summarized in a multi-objective optimisation problem where especially the lateral steady state behaviour is improved by an adaption strategy based on measurable uncertainties. The normally distributed uncertainties are generated by optimal Latin hypercube sampling and a response surface based strategy helps to cut down time consuming model evaluations which offers the possibility to use a genetic optimisation algorithm. Optimisation results are discussed in different criterion spaces and the achieved improvements confirm the validity of the proposed procedure.