MTL robust testing and verification for LPV systems

Fainekos, Georgios; Pappas, George J.

doi:10.1109/acc.2009.5159969

Cited by 3 publications

(2 citation statements)

References 26 publications

(34 reference statements)

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“…Therefore, recent research efforts have been invested in property falsification methods [9]- [12]. In falsification, the space of operating conditions and/or inputs is searched in order to find an initial condition and/or parameter that will force the system to exhibit an unsafe behavior with respect to the formal requirement.…”

Section: Introductionmentioning

confidence: 99%

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Computing descent direction of MTL robustness for non-linear systems

Abbas

Fainekos

2013

2013 American Control Conference

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Abstract-The automatic analysis of transient properties of nonlinear dynamical systems is a challenging problem. The problem is even more challenging when complex state-space and timing requirements must be satisfied by the system. Such complex requirements can be captured by Metric Temporal Logic (MTL) specifications. The problem of finding system behaviors that do not satisfy an MTL specification is referred to as MTL falsification. This paper presents an approach for improving stochastic MTL falsification methods by performing local search in the set of initial conditions. In particular, MTL robustness quantifies how correct or wrong is a system trajectory with respect to an MTL specification. Positive values indicate satisfaction of the property while negative values indicate falsification. A stochastic falsification method attempts to minimize the system's robustness with respect to the MTL property. Given some arbitrary initial state, this paper presents a method to compute a descent direction in the set of initial conditions, such that the new system trajectory gets closer to the unsafe set of behaviors. This technique can be iterated in order to converge to a local minimum of the robustness landscape. The paper demonstrates the applicability of the method on some challenging nonlinear systems from the literature. I. INTRODUCTIONA number of applications can only be accurately modeled using nonlinear dynamical models. Typical such applications include analog circuits [1]-[3] and biological and medical systems [4]- [7]. A common theme of all the aforementioned applications is the need to verify transient or periodic properties of the system. Such properties might involve sequencing of events, conditional reachability and invariants and realtime constraints and can be formally captured using temporal logics [4], [8].Unfortunately, for complex nonlinear systems, these types of properties are hard -if not impossible -to verify algorithmically. Therefore, recent research efforts have been invested in property falsification methods [9]-[12]. In falsification, the space of operating conditions and/or inputs is searched in order to find an initial condition and/or parameter that will force the system to exhibit an unsafe behavior with respect to the formal requirement. In turn, the unsafe system trajectory can be used in order to manually or automatically modify the system to achieve the desired system behavior and performance [13], [14].In [10], [15], the temporal logic falsification problem is converted into an optimization (minimization) problem based on the notion of robustness of temporal logics [16]. Essentially, a system trajectory with negative robustness is one that proves the existence of unsafe system behaviors.

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Section: Introductionmentioning

confidence: 99%

“…MTL properties of nonlinear systems have been studied in [12] through abstractions to Linear Parameter Varying (LPV) systems. The work in [11] studies the applicability of statistical model checking methods on stochastic hybrid systems.…”

Section: Introductionmentioning

confidence: 99%

Computing descent direction of MTL robustness for non-linear systems

Abbas

Fainekos

2013

2013 American Control Conference

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Tolerance of Reinforcement Learning Controllers Against Deviations in Cyber Physical Systems

Zhang,

Kapoor,

Meira-Góes

et al. 2024

Lecture Notes in Computer Science

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Cyber-physical systems (CPS) with reinforcement learning (RL)-based controllers are increasingly being deployed in complex physical environments such as autonomous vehicles, the Internet-of-Things (IoT), and smart cities. An important property of a CPS is tolerance; i.e., its ability to function safely under possible disturbances and uncertainties in the actual operation. In this paper, we introduce a new, expressive notion of tolerance that describes how well a controller is capable of satisfying a desired system requirement, specified using Signal Temporal Logic (STL), under possible deviations in the system. Based on this definition, we propose a novel analysis problem, called the tolerance falsification problem, which involves finding small deviations that result in a violation of the given requirement. We present a novel, two-layer simulation-based analysis framework and a novel search heuristic for finding small tolerance violations. To evaluate our approach, we construct a set of benchmark problems where system parameters can be configured to represent different types of uncertainties and disturbances in the system. Our evaluation shows that our falsification approach and heuristic can effectively find small tolerance violations.

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