Self-optimisation relies on the degree of object-to-object transparency obtained about the behaviour of the involved subsystems. The scientific challenge is to reveal the structural stability of the model and the production system, which changes with type and number of involved subsystems and their interaction at the actual process state. The adapted model structures have to be that tolerant and robust, that unavoidable changes of the process parameters and uncertainties about the material state are not essential for the system sensitivity and will not lead to process malfunctions. The iteratively increased extent of embedded cognition and features of self-optimisation are expected to enhance a more systematic development and evaluation of system solutions for fast ramp-up. Model based diagnosis and demand-adapted, reduced models are combined to enable the foreseen cognitive system to be re-adjustable to support the design of robust, controlled, and efficient rigging, monitoring and control systems.

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