2021/11/17 by Xiaowu Sun, Sun, Xiaowu, Yasser Shoukry +1
Computer Science · Physics and Astronomy · #FOS: Electrical engineering #Machine Learning and Algorithms #Model Reduction and Neural Networks #Neural Networks and Applications #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2111.08853
openalex publication_date 2021/11/17 · openalex created_date 2021/11/22 · openalex updated_date 2026/07/28
In this paper, we introduce NNSynth, a new framework that uses machine learning techniques to guide the design of abstraction-based controllers with correctness guarantees. NNSynth utilizes neural networks (NNs) to guide the search over the space of controllers. The trained neural networks are "projected" and used for constructing a "local" abstraction of the system. An abstraction-based controller is then synthesized from such "local" abstractions. If a controller that satisfies the specifications is not found, then the best found controller is "lifted" to a neural network for additional training. Our experiments show that this neural network-guided synthesis leads to more than 50× or even 100× speedup in high dimensional systems compared to the state-of-the-art.