Computationally Efficient Safe Control of Linear Systems under Severe Sensor Attacks

TL;DR

This paper introduces a computationally efficient method for ensuring safety in linear systems under severe sensor attacks by directly computing control actions from input-output data, avoiding expensive secure state reconstruction.

Contribution

The paper proposes a novel control design that bypasses full secure state reconstruction, using conservative bounds on control barrier functions for real-time safety assurance.

Findings

Achieves safety guarantees with reduced computational complexity.

Outperforms traditional SSR methods in numerical comparisons.

Provides a scalable approach for severe sensor attack scenarios.

Abstract

Cyber-physical systems are prone to sensor attacks that can compromise safety. A common approach to synthesizing controllers robust to sensor attacks is secure state reconstruction (SSR) -- but this is computationally expensive, hindering real-time control. In this paper, we take a safety-critical perspective on mitigating severe sensor attacks, leading to a computationally efficient solution. Namely, we design feedback controllers that ensure system safety by directly computing control actions from past input-output data. Instead of fully solving the SSR problem, we use conservative bounds on a control barrier function (CBF) condition, which we obtain by extending the recent eigendecomposition-based SSR approach to severe sensor attack settings. Additionally, we present an extended approach that solves a smaller-scale subproblem of the SSR problem, taking on some computational burden to mitigate the conservatism in the main approach. Numerical comparisons confirm that the traditional SSR approaches suffer from combinatorial issues, while our approach achieves safety guarantees with greater computational efficiency.