Optimal Symbolic Controllers Determinization for BDD storage
This work addresses the challenge of storing controllers on embedded platforms, which is an incremental improvement for control synthesis tools.
The paper tackles the problem of large controller sizes in symbolic controller synthesis by proposing new determinization algorithms, achieving up to 85% reduction in controller size compared to previous methods.
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.