A Timing-Based Framework for Designing Resilient Cyber-Physical Systems under Safety Constraint

TL;DR

This paper introduces a timing-based framework for analyzing and designing resilient cyber-physical systems (CPS) that ensures safety across various architectures, using hybrid system models and control policy computation, validated through vehicle cruise control case study.

Contribution

It develops a unified hybrid system model and a common methodology for safety analysis and control policy design applicable to multiple resilient CPS architectures.

Findings

Framework effectively models different resilient architectures.

Algorithm computes control policies satisfying safety constraints.

Applicable to CPS with polynomial dynamics and adaptable to new architectures.

Abstract

Cyber-physical systems (CPS) are required to satisfy safety constraints in various application domains such as robotics, industrial manufacturing systems, and power systems. Faults and cyber attacks have been shown to cause safety violations, which can damage the system and endanger human lives. Resilient architectures have been proposed to ensure safety of CPS under such faults and attacks via methodologies including redundancy and restarting from safe operating conditions. The existing resilient architectures for CPS utilize different mechanisms to guarantee safety, and currently there is no approach to compare them. Moreover, the analysis and design undertaken for CPS employing one architecture is not readily extendable to another. In this paper, we propose a timing-based framework for CPS employing various resilient architectures and develop a common methodology for safety analysis and computation of control policies and design parameters. Using the insight that the cyber subsystem operates in one out of a finite number of statuses, we first develop a hybrid system model that captures CPS adopting any of these architectures. Based on the hybrid system, we formulate the problem of joint computation of control policies and associated timing parameters for CPS to satisfy a given safety constraint and derive sufficient conditions for the solution. Utilizing the derived conditions, we provide an algorithm to compute control policies and timing parameters relevant to the employed architecture. We also note that our solution can be applied to a wide class of CPS with polynomial dynamics and also allows incorporation of new architectures. We verify our proposed framework by performing a case study on adaptive cruise control of vehicles.