Help Privacy Policy Disclaimer
  Advanced SearchBrowse





Data-Driven Abstraction-Based Control Synthesis


Majumdar,  Rupak
Group R. Majumdar, Max Planck Institute for Software Systems, Max Planck Society;


Salamati,  Mahmoud
Group R. Majumdar, Max Planck Institute for Software Systems, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

(Preprint), 5MB

Supplementary Material (public)
There is no public supplementary material available

Kazemi, M., Majumdar, R., Salamati, M., Soudjani, S., & Wooding, B. (2022). Data-Driven Abstraction-Based Control Synthesis. Retrieved from https://arxiv.org/abs/2206.08069.

Cite as: https://hdl.handle.net/21.11116/0000-000B-5E5F-3
This paper studies formal synthesis of controllers for continuous-space
systems with unknown dynamics to satisfy requirements expressed as linear
temporal logic formulas. Formal abstraction-based synthesis schemes rely on a
precise mathematical model of the system to build a finite abstract model,
which is then used to design a controller. The abstraction-based schemes are
not applicable when the dynamics of the system are unknown. We propose a
data-driven approach that computes the growth bound of the system using a
finite number of trajectories. The growth bound together with the sampled
trajectories are then used to construct the abstraction and synthesise a
Our approach casts the computation of the growth bound as a robust convex
optimisation program (RCP). Since the unknown dynamics appear in the
optimisation, we formulate a scenario convex program (SCP) corresponding to the
RCP using a finite number of sampled trajectories. We establish a sample
complexity result that gives a lower bound for the number of sampled
trajectories to guarantee the correctness of the growth bound computed from the
SCP with a given confidence. We also provide a sample complexity result for the
satisfaction of the specification on the system in closed loop with the
designed controller for a given confidence. Our results are founded on
estimating a bound on the Lipschitz constant of the system and provide
guarantees on satisfaction of both finite and infinite-horizon specifications.
We show that our data-driven approach can be readily used as a model-free
abstraction refinement scheme by modifying the formulation of the growth bound
and providing similar sample complexity results. The performance of our
approach is shown on three case studies.