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Uncertainty treatment in self-optimising systems touches two design-issues. Firstly, a valid estimation of uncertainties within the system is impossible beforehand as the uncertainties as well as the systems behaviour changes during run-time due to self-optimisation. Secondly, the design of a selfoptimising system needs to mediate between the often conflicting goals of optimality and robustness. Here we present the concept for a lightweight algorithmic add-on for self-optimising function approximators that enables to reflect uncertainties related to the current state and to flexibly combine optimality and robustness in one design. Illustrating examples of TS-fuzzy systems highlight the properties of our approach.

Robustification of Self-Optimising Systems via Explicit Treatment of Uncertain Information