Combined Left and Right Temporal Robustness for Control Under STL Specifications

Rodionova, Alëna; Lindemann, Lars; Morari, Manfred; Pappas, George J.

doi:10.1109/lcsys.2022.3209928

Cited by 14 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, our work considers the presence of an adversary, while [9] assumes a single agent. Robust control under signal temporal logic (STL) formulae has been studied based on notions of space robustness [35,36] and temporal robustness [37,38]. These works did not consider the presence of an adversary.…”

Section: Related Workmentioning

confidence: 99%

Robust Satisfaction of Metric Interval Temporal Logic Objectives in Adversarial Environments

Niu

Ramasubramanian

Clark

et al. 2023

Games

View full text Add to dashboard Cite

This paper studies the synthesis of controllers for cyber-physical systems (CPSs) that are required to carry out complex time-sensitive tasks in the presence of an adversary. The time-sensitive task is specified as a formula in the metric interval temporal logic (MITL). CPSs that operate in adversarial environments have typically been abstracted as stochastic games (SGs); however, because traditional SG models do not incorporate a notion of time, they cannot be used in a setting where the objective is time-sensitive. To address this, we introduce durational stochastic games (DSGs). DSGs generalize SGs to incorporate a notion of time and model the adversary’s abilities to tamper with the control input (actuator attack) and manipulate the timing information that is perceived by the CPS (timing attack). We define notions of spatial, temporal, and spatio-temporal robustness to quantify the amounts by which system trajectories under the synthesized policy can be perturbed in space and time without affecting satisfaction of the MITL objective. In the case of an actuator attack, we design computational procedures to synthesize controllers that will satisfy the MITL task along with a guarantee of its robustness. In the presence of a timing attack, we relax the robustness constraint to develop a value iteration-based procedure to compute the CPS policy as a finite-state controller to maximize the probability of satisfying the MITL task. A numerical evaluation of our approach is presented on a signalized traffic network to illustrate our results.

show abstract

Section: Related Workmentioning

confidence: 99%

Robust Satisfaction of Metric Interval Temporal Logic Objectives in Adversarial Environments

Niu

Ramasubramanian

Clark

et al. 2023

Games

View full text Add to dashboard Cite

show abstract

“…This signed distance is called the robustness value. There are a number of ways to define the quantitative semantics of STL [8], [14], [37], [1]; in this paper, we focus on the semantics from [8] that we reproduce below. The robustness value ρ(ϕ, σ s0 , k) of an STL formula ϕ over a trajectory σ s0 at time k can be defined recursively as follows.…”

Section: Preliminariesmentioning

confidence: 99%

A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systems

Hashemi¹,

Hoxha²,

Yamaguchi³

et al. 2023

Preprint

View full text Add to dashboard Cite

Signal Temporal Logic (STL) has become a popular tool for expressing formal requirements of Cyber-Physical Systems (CPS). The problem of verifying STL properties of neural network-controlled CPS remains a largely unexplored problem. In this paper, we present a model for the verification of Neural Network (NN) controllers for general STL specifications using a custom neural architecture where we map an STL formula into a feed-forward neural network with ReLU activation. In the case where both our plant model and the controller are ReLU-activated neural networks, we reduce the STL verification problem to reachability in ReLU neural networks. We also propose a new approach for neural network controllers with general activation functions; this approach is a sound and complete verification approach based on computing the Lipschitz constant of the closed-loop control system. We demonstrate the practical efficacy of our techniques on a number of examples of learningenabled control systems.

show abstract

“…It maximizes the right-time robustness of an STL specification of a discrete linear system. A left-right combined time robustness notion is proposed in [25] to address the weakness of left-and right-time robustness for a single-directional time shift. It also proposes a control algorithm for linear systems that maximizes the combined time robustness using MILP.…”

Section: Related Workmentioning

confidence: 99%

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

Chen¹,

Smolka²,

Paoletti³

et al. 2022

Preprint

View full text Add to dashboard Cite

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 (rec, dur ) real-valued pairs, where rec represents the system's capability to rapidly recover from a property violation (recoverability), and 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 𝜖-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.

show abstract

Combined Left and Right Temporal Robustness for Control Under STL Specifications

Cited by 14 publications

References 32 publications

Robust Satisfaction of Metric Interval Temporal Logic Objectives in Adversarial Environments

Robust Satisfaction of Metric Interval Temporal Logic Objectives in Adversarial Environments

A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systems

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

Contact Info

Product

Resources

About