Feedback Control for Statistical Model Checking of Cyber-Physical Systems

Kalajdzic, Kenan; Jégourel, Cyrille; Lukina, Anna; Bartocci, Ezio; Legay, Axel; Smolka, Scott A.; Grosu, Radu

doi:10.1007/978-3-319-47166-2_4

Cited by 14 publications

(6 citation statements)

References 16 publications

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“…However, the optimal importance sampling distribution is not a distribution family from the system path space. Kalajdzic et al [42] proposed an SMC method based on the principle of feedback control. is method learns a model of a cyber-physical fusion system by Input: N, the number of samples per iteration.…”

Section: Related Workmentioning

confidence: 99%

Towards a Statistical Model Checking Method for Safety-Critical Cyber-Physical System Verification

Xie

Tan

Fang

et al. 2021

Security and Communication Networks

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Safety-Critical Cyber-Physical System (SCCPS) refers to the system that if the system fails or its key functions fail, it will cause casualties, property damage, environmental damage, and other catastrophic consequences. Therefore, it is vital to verify the safety of safety critical systems. In the community, the SCCPS safety verification mainly relies on the statistical model checking methodology, but for SCCPS with extremely high safety requirements, the statistical model checking method is difficult/infeasible to sample the extremely small probability event since the probability of the system violating the safety is very low (rare property). In response to this problem, we propose a new method of statistical model checking for high-safety SCCPS. Firstly, with the CTMC-approximated SCCPS path probability space model, it leverages the maximum likelihood estimation method to learn the parameters of CTMC. Then, the embedded DTMC can be derived from CTMC, and a cross-entropy optimization model based on DTMC can be constructed. Finally, we propose an algorithm of iteratively learning the optimal importance sampling distribution on the discrete path space and an algorithm to check the statistical model of verifying the rare attribute. Eventually, experimental results show that the method proposed in this paper can effectively verify the rare attributes of SCCPS. Under the same sample size, comparing with the heuristic importance sampling methods, the estimated value of this method can be better distributed around the mean value, and the related standard deviation and relative error are reduced by more than an order of magnitude.

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Section: Related Workmentioning

confidence: 99%

Towards a Statistical Model Checking Method for Safety-Critical Cyber-Physical System Verification

Xie

Tan

Fang

et al. 2021

Security and Communication Networks

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“…In [31], a framework utilizing statistical model checking for dependability within railway systems is developed. In addition, rare events problems in cyberphysical applications have been emphasized in statistical model checking, either by adding feedback control to efficiently estimate probabilities [32] by importance sampling and Cross-Entropy methods [33], or by importance splitting and reformulating rare probabilities [34]. Also within software engineering, the ActivFORMS [35] framework exploits statistical model checking at runtime to select configurations that comply with self-adaptation goals over an internet-of-things network topology.…”

Section: Related Workmentioning

confidence: 99%

Early Validation of Cyber-Physical Space Systems via Multi-Concerns Integration

Tsigkanos

Jin

et al. 2020

Preprint

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Cyber-physical space systems are engineered systems operating within physical space with design requirements that depend on space, e.g., regarding location or movement behavior. They are built from and depend upon the seamless integration of computation and physical components. Typical examples include systems where software-driven agents such as mobile robots explore space and perform actions to complete particular missions. Design of such a system often depends on multiple concerns expressed by different stakeholders, capturing different aspects of the system. We propose a model-driven approach supporting (a) separation of concerns during design, (b) systematic and semi-automatic integration of separately modeled concerns, and finally (c) early validation via statistical model checking. We evaluate our approach over two different case studies of cyber-physical space systems.

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“…The resulting probability of the rare event is then calculated as the product p = k i=1 p i of the intermediate probabilities. The levels can be defined adaptively [23].…”

Section: Importance Splittingmentioning

confidence: 99%

“…Although it is a very powerful optimization technique, it has not yet been possible to achieve a high success rate in solving the considered flocking problem. Sequential Monte-Carlo methods proved to be efficient in tackling the question of control for linear stochastic systems [9], in particular, Importance Splitting (IS) [23]. The approach we propose is, however, the first attempt to combine adaptive IS, PSO, and receding-horizon technique for synthesis of optimal plans for controllable systems.…”

Section: Related Workmentioning

confidence: 99%

ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans

Lukina

Esterle

Hirsch

et al. 2017

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. We introduce ARES, an efficient approximation algorithm for generating optimal plans (action sequences) that take an initial state of a Markov Decision Process (MDP) to a state whose cost is below a specified (convergence) threshold. ARES uses Particle Swarm Optimization, with adaptive sizing for both the receding horizon and the particle swarm. Inspired by Importance Splitting, the length of the horizon and the number of particles are chosen such that at least one particle reaches a next-level state, that is, a state where the cost decreases by a required delta from the previous-level state. The level relation on states and the plans constructed by ARES implicitly define a Lyapunov function and an optimal policy, respectively, both of which could be explicitly generated by applying ARES to all states of the MDP, up to some topological equivalence relation. We also assess the effectiveness of ARES by statistically evaluating its rate of success in generating optimal plans. The ARES algorithm resulted from our desire to clarify if flying in V-formation is a flocking policy that optimizes energy conservation, clear view, and velocity alignment. That is, we were interested to see if one could find optimal plans that bring a flock from an arbitrary initial state to a state exhibiting a single connected V-formation. For flocks with 7 birds, ARES is able to generate a plan that leads to a V-formation in 95% of the 8,000 random initial configurations within 63 seconds, on average. ARES can also be easily customized into a model-predictive controller (MPC) with an adaptive receding horizon and statistical guarantees of convergence. To the best of our knowledge, our adaptive-sizing approach is the first to provide convergence guarantees in receding-horizon techniques.

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Feedback Control for Statistical Model Checking of Cyber-Physical Systems

Cited by 14 publications

References 16 publications

Towards a Statistical Model Checking Method for Safety-Critical Cyber-Physical System Verification

Towards a Statistical Model Checking Method for Safety-Critical Cyber-Physical System Verification

Early Validation of Cyber-Physical Space Systems via Multi-Concerns Integration

ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans

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