Learning and Verification of Feedback Control Systems using Feedforward Neural Networks

By Souradeep Dutta, Susmit Jha, Sriram Sankaranarayanan, Ashish Tiwari. 2018

74 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistical and Nonlinear Physics

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In Artificial Intelligence

In Control and Systems Engineering

Mykel J. Kochenderfer

In Statistical and Nonlinear Physics

Mykel J. Kochenderfer

Citations in the corpus

Formal Security Analysis of Neural Networks using Symbolic Intervals (2018, background): “Recently, Mixed Integer Linear programming (MILP) solvers combined with gradient descent have also been proposed for verification of DNNs [9, 10].”
Algorithms for Verifying Deep Neural Networks (2019, background): “• closed-loop safety, stability and robustness by executing control policies defined by neural networks[11], [45], or verification of recurrent neural networks [2];”