# Automated Formal Synthesis of Digital Controllers for State-Space Physical Plants

arXiv: https://arxiv.org/abs/1705.00981 · HTML: https://mlsanity.com/paper/1705.00981/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2017.

## Built on by (most cited first, 1 of 1)

- [Floating-Point Neural Network Verification at the Software Level](https://mlsanity.com/paper/2510.23389/) (2025)

## Abstract (arXiv)

We present a sound and automated approach to synthesize safe digital feedback controllers for physical plants represented as linear, time invariant models. Models are given as dynamical equations with inputs, evolving over a continuous state space and accounting for errors due to the digitalization of signals by the controller. Our approach has two stages, leveraging counterexample guided inductive synthesis (CEGIS) and reachability analysis. CEGIS synthesizes a static feedback controller that stabilizes the system under restrictions given by the safety of the reach space. Safety is verified either via BMC or abstract acceleration; if the verification step fails, we refine the controller by generalizing the counterexample. We synthesize stable and safe controllers for intricate physical plant models from the digital control literature.