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One of the main objectives of fuzzy control is to translate expert rules - formulated in imprecise ("fuzzy") words from natural language - into a precise control strategy. This translation is usually done is two steps. First, we apply a fuzzy control methodology to get a rough approximation to the expert's control strategy, and then we tune the resulting fuzzy control system. The first step (getting a rough approximation) is well-analyzed, and the fact that we have expert's intuitive understanding enables us to use soft computing techniques to perform this step. The second (tuning) step is much more difficult: we no longer have any expert understanding of which tuning is better, and therefore, soft computing techniques are not that helpful. In this paper, we show that we can formulate an important particular case of the tuning problem as a traditional optimization problem and solve it by using traditional ("hard computing") techniques. We show, on a practical industrial control example, that the resulting fusion of soft computing (for a rough approximation) and a hard computing (for tuning) leads to a high quality control.