An STL-based Approach to Resilient Control for Cyber-Physical Systems

TL;DR

ResilienC is a formal framework that optimizes control strategies for cyber-physical systems to maximize their ability to recover from violations and avoid future violations, demonstrated on autonomous vehicles and package delivery.

Contribution

The paper introduces ResilienC, a novel formalism and optimization method for resilient control of CPS based on STL specifications and Pareto-optimal solutions.

Findings

Effective control strategies for CPS resilience were derived.

ResilienC successfully applied to autonomous vehicle lane keeping.

Framework demonstrated on deadline-driven package delivery.

Abstract

We present ResilienC, a framework for resilient control of Cyber-Physical Systems subject to STL-based requirements. ResilienC utilizes a recently developed formalism for specifying CPS resiliency in terms of sets of $(\mathit{rec},\mathit{dur})$ real-valued pairs, where $\mathit{rec}$ represents the system's capability to rapidly recover from a property violation (recoverability), and $\mathit{dur}$ is reflective of its ability to avoid violations post-recovery (durability). We define the resilient STL control problem as one of multi-objective optimization, where the recoverability and durability of the desired STL specification are maximized. When neither objective is prioritized over the other, the solution to the problem is a set of Pareto-optimal system trajectories. We present a precise solution method to the resilient STL control problem using a mixed-integer linear programming encoding and an a posteriori $\epsilon$-constraint approach for efficiently retrieving the complete set of optimally resilient solutions. In ResilienC, at each time-step, the optimal control action selected from the set of Pareto-optimal solutions by a Decision Maker strategy realizes a form of Model Predictive Control. We demonstrate the practical utility of the ResilienC framework on two significant case studies: autonomous vehicle lane keeping and deadline-driven, multi-region package delivery.