Don't mistake the statistical identification requirement that a reference category fulfils with the analysis of an experiment that has a control condition.
Consider first this identification requirement: Because the probabilities of $K=6$ categories have to add up to 1 for every observation, there must be a linear constraint added somewhere. For the regression coefficients $\beta_1 \ldots \beta_K$, we can state this constraint as $\sum^K_i a_i \beta_i = 0$ for some fixed $a$, not all zero. One rather easy way to fulfil this is to set $a_K=1$ and the rest to 0. That effectively sets $\beta_K$ to 0, and makes $K$ the reference category. But you can probably see other ways to fulfil the constraint. For example, we could force the $\beta$s to sum to zero, ANOVA-style. Although these identification options alter the numerical values of the $\beta$s and some choices make estimation easier than others, they don't affect the predicted probabilities that the model produces, so in a fairly strong sense they don't matter and aren't part of the model's assumptions.
Because of this, even when you have a control condition to compare other conditions to and you have decided to pick a reference category then it's at best a reporting convenience, not some deep mathematical requirement, that you make the reference category the one that represents the control condition. You can model experiments just as well if you don't line these things up.
It also implies that you can duck the entire question of how your parameters were coded by reporting predictive probabilities under various combinations of explanatory variables. Then it no longer matters whether there is a natural baseline condition because you don't have to spend your time interpreting parameters at all (although of course you still want to report them). A good paper on this approach is Fox and Anderson.