  Controller synthesis techniques based on symbolic abstractions appeal by
producing correct-by-design controllers, under intricate behavioural
constraints. Yet, being relations between abstract states and inputs, such
controllers are immense in size, which makes them futile for em- bedded
platforms. Control-synthesis tools such as PESSOA, SCOTS, and CoSyMA tackle the
problem by storing controllers as binary decision di- agrams (BDDs). However,
due to redundantly keeping multiple inputs per-state, the resulting controllers
are still too large. In this work, we first show that choosing an optimal
controller determinization is an NP- complete problem. Further, we consider the
previously known controller determinization technique and discuss its
weaknesses. We suggest several new approaches to the problem, based on greedy
algorithms, symbolic regression, and (muli-terminal) BDDs. Finally, we
empirically compare the techniques and show that some of the new algorithms can
produce up to 85% smaller controllers than those obtained with the previous
technique.
