Learning and Verification of Feedback Control Systems using Feedforward Neural Networks. Issue 16 (2018)

• Record Type: Journal Article
• Authors: Dutta, Souradeep; Jha, Susmit; Sankaranarayanan, Sriram; Tiwari, Ashish
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 16(2018)
• Page Start: 151
• Page End: 156
• Publication Date: 2018
• Subjects: Reachability -- safety analysis -- verification -- formal synthesis -- neural networks; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.08.026
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: We present an approach to learn and formally verify feedback laws for data-driven models of neural networks. Neural networks are emerging as powerful and general data-driven representations for functions. This has led to their increased use in data-driven plant models and the representation of feedback laws in control systems. However, it is hard to formally verify properties of such feedback control systems. The proposed learning approach uses a receding horizon formulation that samples from the initial states and disturbances to enforce properties such as reachability, safety and stability. Next, our verification approach uses an over-approximate reachability analysis over the system, supported by range analysis for feedforward neural networks. We report promising results obtained by applying our techniques on several challenging nonlinear dynamical systems.