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Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
Paulo Tabuada, Bahman Gharesifard
In this paper, we explain the universal approximation capabilities of deep residual neural networks through geometric nonlinear control. Inspired by recent work establishing links…
Distortion based Light-weight Security for Cyber-Physical Systems
Gaurav Kumar Agarwal, Mohammed Karmoose, Suhas Diggavi +2
In Cyber-Physical Systems (CPS), inference based on communicated data is of critical significance as it can be used to manipulate or damage the control operations by adversaries. T…
Privacy Against Adversarial Classification in Cyber-Physical Systems
Carlos Murguia, Paulo Tabuada
For a class of Cyber-Physical Systems (CPSs), we address the problem of performing computations over the cloud without revealing private information about the structure and operati…
Control Barrier Function based Quadratic Programs Introduce Undesirable Asymptotically Stable Equilibria
Matheus F. Reis, A. Pedro Aguiar, Paulo Tabuada
Control Lyapunov functions (CLFs) and control barrier functions (CBFs) have been used to develop provably safe controllers by means of quadratic programs (QPs), guaranteeing safety…
Rapid Top-Down Synthesis of Large-Scale IoT Networks
Pradipta Ghosh, Jonathan Bunton, Dimitrios Pylorof +6
Advances in optimization and constraint satisfaction techniques, together with the availability of elastic computing resources, have spurred interest in large-scale network verific…
Data-driven Stabilization of SISO Feedback Linearizable Systems
Lucas Fraile, Matteo Marchi, Paulo Tabuada
In this paper we propose a methodology for stabilizing single-input single-output feedback linearizable systems when no system model is known and no prior data is available to iden…