Optimal symbolic controllers determinization for BDD storage

Ivan S. Zapreev, Cees Verdier, Manuel Mazo

Research output: Contribution to journalConference articleScientificpeer-review

1 Citation (Scopus)
6 Downloads (Pure)


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 embedded platforms. Control-synthesis tools such as PESSOA, SCOTS, and CoSyMA tackle the problem by storing controllers as binary decision diagrams (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.

Original languageEnglish
Pages (from-to)1-6
Issue number16
Publication statusPublished - 2018


  • control law
  • data compression
  • determinism
  • embedded systems
  • genetic algorithms

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