Safety-Critical Online Control with Adversarial Disturbances

• Main Authors: Ramasubramanian, Bhaskar, Xiao, Baicen, Bushnell, Linda, Poovendran, Radha
• Format: Conference Proceeding
• Language: English
• Subjects: Kalman filters; Linear systems; Mathematical model; Riccati equations; Safety; Upper bound; White noise
• ISSN: 2576-2370
• DOI: 10.1109/CDC42340.2020.9304458

This paper studies the control of safety-critical dynamical systems in the presence of adversarial disturbances. We seek to synthesize state-feedback controllers to minimize a cost incurred due to the disturbance while respecting the safety constraint. The safety constraint is given by a bound on an {{\mathcal{H}}_\infty } norm, while the cost is specified as an upper bound on an {{\mathcal{H}}_2} norm of the system. We consider an online setting where costs at each time are revealed only after the controller at that time is chosen. We propose an iterative approach to the synthesis of the controller by solving a modified discrete-time Riccati equation. Solutions of this equation enforce the safety constraint. We compare the cost of this controller with that of the optimal controller when one has complete knowledge of disturbances and costs in hindsight. We show that the regret function, which is defined as the difference between these costs, called varies logarithmically with the time horizon. We validate our approach on a process control setup that is subject to two kinds of adversarial attacks.