On-the-Fly Control Software Synthesis

Alimguzhin, Vadim; Mari, Federico; Melatti, Igor; Salvo, Ivano; Tronci, Enrico

doi:10.1007/978-3-642-39176-7_5

Cited by 9 publications

(6 citation statements)

References 33 publications

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“…The main differences between the above approaches and ours are: (i) they focus on finite state systems whereas we focus on infinite state systems (namely, hybrid systems); (ii) they are white-box (requiring availability of the system model) whereas we are black-box. As for hybrid systems, synergies between explicit and symbolic model checking methods have been investigated in [50,51,52,53,54,55,56,57] in the context of automatic synthesis of controllers for discrete time linear hybrid systems.…”

Section: Related Workmentioning

confidence: 99%

SyLVaaS: System Level Formal Verification as a Service*

Mancini

Mari

Massini

et al. 2016

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The goal of System Level Formal Verification is to show system correctness notwithstanding uncontrollable events (disturbances), as for example faults, variations in system parameters, external inputs, etc. This may be achieved with an exhaustive Hardware In the Loop Simulation based approach, by considering all relevant scenarios in the System Under Verification (SUV) operational environment.In this paper, we present SyLVaaS, a Web-based tool enabling Verification as a Service (VaaS). SyLVaaS implements an assume-guarantee approach to (Hardware In the Loop Simulation based) System Level Formal Verification.SyLVaaS takes as input a finite state automaton defining the SUV operational environment and computes, using parallel algorithms deployed in a cluster infrastructure, a set of highly optimised simulation campaigns, which can be executed in an embarrassingly parallel fashion (i.e., with no communication among the parallel processes) on a set of Simulink instances, using a platform independent Simulink driver downloadable from the SyLVaaS Web site.

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Section: Related Workmentioning

confidence: 99%

SyLVaaS: System Level Formal Verification as a Service*

Mancini

Mari

Massini

et al. 2016

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“…Note that indeed DAPP synthesizes price policies starting from system level formal specifications. See [19], [20], [21], [22], [23] for related work on synthesis from system level formal specifications, which however focuses on synthesis of controllers for hybrid systems. See instead [24], [25], [26], [27] for related work on verification of system level formal specifications.…”

Section: B Related Workmentioning

confidence: 99%

User Flexibility Aware Price Policy Synthesis for Smart Grids

Mancini¹,

Mari²,

Melatti³

et al. 2015

2015 Euromicro Conference on Digital System Design

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In order to optimally manage a modern electricity distribution network, peaks in residential users demand should be avoided, as this can reduce energy and network asset management costs. Furthermore, this must be done without compressing residential users demand. To this aim, in a demand response setting, residential users are given a price policy, which economically motivates them to shift their loads in order to achieve this goal. However, if the price policy for all users is similar, this demand response may result in simply shifting the demand peaks (peak rebound), leaving the problem unsolved. In this paper we propose a novel methodology which i) for each network substation s, automatically computes the desired power profile to be kept in order to optimally manage the network itself; ii) for each network substation s, automatically synthesizes individualized price policies for residential users connected to s, so that s is kept at the desired profile. Note that price policies individualization avoids the peak rebound problem, as different users have different low tariff areas. Furthermore, our methodology measures the flexibility of a residential user as the capacity needed by a home energy storage system (e.g., a battery) to always follow the given price policy, thus mitigating residential users discomfort. We show the feasibility of our approach on a realistic scenario taken from an existing medium voltage Danish distribution network.

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“…In a discrete time setting this problem has been addressed, for piecewise affine systems and safety properties, in [30], [2], [1], [31], [4], [3], [32], [8].…”

Section: Related Workmentioning

confidence: 99%

Patient-specific models from inter-patient biological models and clinical records

Tronci

Mancini

Salvo

et al. 2014

2014 Formal Methods in Computer-Aided Design (FMCAD)

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Abstract-One of the main goals of systems biology models in a health-care context is to individualise models in order to compute patient-specific predictions for the time evolution of species (e.g., hormones) concentrations. In this paper we present a statistical model checking based approach that, given an interpatient model and a few clinical measurements, computes a value for the model parameter vector (model individualisation) that, with high confidence, is a global minimum for the function evaluating the mismatch between the model predictions and the available measurements. We evaluate effectiveness of the proposed approach by presenting experimental results on using the GynCycle model (describing the feedback mechanisms regulating a number of reproductive hormones) to compute patientspecific predictions for the time evolution of blood concentrations of E2 (Estradiol), P4 (Progesterone), FSH (Follicle-Stimulating Hormone) and LH (Luteinizing Hormone) after a certain number of clinical measurements.

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On-the-Fly Control Software Synthesis

Cited by 9 publications

References 33 publications

SyLVaaS: System Level Formal Verification as a Service*

SyLVaaS: System Level Formal Verification as a Service*

User Flexibility Aware Price Policy Synthesis for Smart Grids

Patient-specific models from inter-patient biological models and clinical records

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