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Alimguzhin, V.; Mari, F.; Melatti, I.; Salvo, I.; Tronci, E. |
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Title |
Linearising Discrete Time Hybrid Systems |
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Journal Article |
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2017 |
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IEEE Transactions on Automatic Control |
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62 |
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10 |
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5357-5364 |
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Model Based Design approaches for embedded systems aim at generating correct-by-construction control software, guaranteeing that the closed loop system (controller and plant) meets given system level formal specifications. This technical note addresses control synthesis for safety and reachability properties of possibly non-linear discrete time hybrid systems. By means of syntactical transformations that require non-linear terms to be Lipschitz continuous functions, we over-approximate non-linear dynamics with a linear system whose controllers are guaranteed to be controllers of the original system. We evaluate performance of our approach on meaningful control synthesis benchmarks, also comparing it to a state-of-the-art tool. |
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0018-9286 |
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Sapienza @ mari @ ref7902199 |
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164 |
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Author |
Martinelli, Marco; Tronci, Enrico; Dipoppa, Giovanni; Balducelli, Claudio |
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Title |
Electric Power System Anomaly Detection Using Neural Networks |
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2004 |
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8th International Conference on: Knowledge-Based Intelligent Information and Engineering Systems (KES) |
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1242-1248 |
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The aim of this work is to propose an approach to monitor and protect Electric Power System by learning normal system behaviour at substations level, and raising an alarm signal when an abnormal status is detected; the problem is addressed by the use of autoassociative neural networks, reading substation measures. Experimental results show that, through the proposed approach, neural networks can be used to learn parameters underlaying system behaviour, and their output processed to detecting anomalies due to hijacking of measures, changes in the power network topology (i.e. transmission lines breaking) and unexpected power demand trend. |
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Springer |
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Wellington, New Zealand |
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Negoita, M.G.; Howlett, R.J.; Jain, L.C. |
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Lecture Notes in Computer Science |
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3213 |
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3-540-23318-0 |
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yes |
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Sapienza @ mari @ kes04 |
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35 |
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Cesta, Amedeo; Fratini, Simone; Orlandini, Andrea; Finzi, Alberto; Tronci, Enrico |
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Title |
Flexible Plan Verification: Feasibility Results |
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Journal Article |
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2011 |
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Fundamenta Informaticae |
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107 |
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2 |
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111-137 |
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yes |
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Sapienza @ mari @ fi11 |
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15 |
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Charme |
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2003 |
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Lecture Notes in Computer Science |
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2860 |
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Springer |
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Geist, D.; Tronci, E. |
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3-540-20363-X |
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yes |
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Sapienza @ mari @ editor-charme03 |
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37 |
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Author |
Tronci, Enrico |
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Title |
Optimal Finite State Supervisory Control |
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1996 |
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CDC '96: Proceedings of the 35th IEEE International Conference on Decision and Control |
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Supervisory Controllers are Discrete Event Dynamic Systems (DEDSs) forming the discrete core of a Hybrid Control System. We address the problem of automatic synthesis of Optimal Finite State Supervisory Controllers (OSCs). We show that Boolean First Order Logic (BFOL) and Binary Decision Diagrams (BDDs) are an effective methodological and practical framework for Optimal Finite State Supervisory Control. Using BFOL programs (i.e. systems of boolean functional equations) and BDDs we give a symbolic (i.e. BDD based) algorithm for automatic synthesis of OSCs. Our OSC synthesis algorithm can handle arbitrary sets of final states as well as plant transition relations containing loops and uncontrollable events (e.g. failures). We report on experimental results on the use of our OSC synthesis algorithm to synthesize a C program implementing a minimum fuel OSC for two autonomous vehicles moving on a 4 x 4 grid. |
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IEEE Computer Society |
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Washington, DC, USA |
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yes |
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Call Number |
Sapienza @ mari @ cdc96 |
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67 |
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Author |
Alimguzhin, Vadim; Mari, Federico; Melatti, Igor; Salvo, Ivano; Tronci, Enrico |
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Title |
A Map-Reduce Parallel Approach to Automatic Synthesis of Control Software |
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Report |
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2012 |
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abs/1210.2276 |
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Abstract |
Many Control Systems are indeed Software Based Control Systems, i.e. control systems whose controller consists of control software running on a microcontroller device. This motivates investigation on Formal Model Based Design approaches for automatic synthesis of control software.
Available algorithms and tools (e.g., QKS) may require weeks or even months of computation to synthesize control software for large-size systems. This motivates search for parallel algorithms for control software synthesis.
In this paper, we present a map-reduce style parallel algorithm for control software synthesis when the controlled system (plant) is modeled as discrete time linear hybrid system. Furthermore we present an MPI-based implementation PQKS of our algorithm. To the best of our knowledge, this is the first parallel approach for control software synthesis.
We experimentally show effectiveness of PQKS on two classical control synthesis problems: the inverted pendulum and the multi-input buck DC/DC converter. Experiments show that PQKS efficiency is above 65%. As an example, PQKS requires about 16 hours to complete the synthesis of control software for the pendulum on a cluster with 60 processors, instead of the 25 days needed by the sequential algorithm in QKS. |
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CoRR, Technical Report |
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yes |
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Call Number |
Sapienza @ mari @ |
Serial |
101 |
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Author |
Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E. |
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Title |
Reconciling interoperability with efficient Verification and Validation within open source simulation environments |
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Journal Article |
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Year |
2021 |
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Simulation Modelling Practice and Theory |
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102277 |
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Simulation, Verification and Validation, Interoperability, FMI/FMU, Model Exchange, Cyber-Physical Systems |
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Abstract |
A Cyber-Physical System (CPS) comprises physical as well as software subsystems. Simulation-based approaches are typically used to support design and Verification and Validation (V&V) of CPSs in several domains such as: aerospace, defence, automotive, smart grid and healthcare. Accordingly, many simulation-based tools are available to support CPS design. This, on one side, enables designers to choose the toolchain that best suits their needs, on the other side poses huge interoperability challenges when one needs to simulate CPSs whose subsystems have been designed and modelled using different toolchains. To overcome such an interoperability problem, in 2010 the Functional Mock-up Interface (FMI) has been proposed as an open standard to support both Model Exchange (ME) and Co-Simulation (CS) of simulation models created with different toolchains. FMI has been adopted by several modelling and simulation environments. Models adhering to such a standard are called Functional Mock-up Units (FMUs). Indeed FMUs play an essential role in defining complex CPSs through, e.g., the System Structure and Parametrization (SSP) standard. Simulation-based V&V of CPSs typically requires exploring different simulation scenarios (i.e., exogenous input sequences to the CPS under design). Many such scenarios have a shared prefix. Accordingly, to avoid simulating many times such shared prefixes, the simulator state at the end of a shared prefix is saved and then restored and used as a start state for the simulation of the next scenario. In this context, an important FMI feature is the capability to save and restore the internal FMU state on demand. This is crucial to increase efficiency of simulation-based V&V. Unfortunately, the implementation of this feature is not mandatory and it is available only within some commercial software. As a result, the interoperability enabled by the FMI standard cannot be fully exploited for V&V when using open-source simulation environments. This motivates developing such a feature for open-source CPS simulation environments. Accordingly, in this paper, we focus on JModelica, an open-source modelling and simulation environment for CPSs based on an open standard modelling language, namely Modelica. We describe how we have endowed JModelica with our open-source implementation of the FMI 2.0 functions needed to save and restore internal states of FMUs for ME. Furthermore, we present experimental results evaluating, through 934 benchmark models, correctness and efficiency of our extended JModelica. Our experimental results show that simulation-based V&V is, on average, 22 times faster with our get/set functionality than without it. |
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1569-190x |
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MCLab @ davi @ Sinisi2021102277 |
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186 |
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Author |
Mancini, T.; Melatti, I.; Tronci, E. |
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Any-horizon uniform random sampling and enumeration of constrained scenarios for simulation-based formal verification |
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2021 |
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IEEE Transactions on Software Engineering |
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1-1 |
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Model-based approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding </i>vacuity</i> in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying all the given constraints is a key enabler for the practical viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution (e.g., uniformly), and can be very inefficient to produce at all scenarios that are both legal (with respect to SUV assumptions) and of interest (with respect to the additional constraints). For example, in our case studies, up to 91% of the scenarios generated using such iterative approaches would need to be neglected. In this article, we show how, given a set of constraints on the input scenarios succinctly defined by multiple finite memory monitors, a data structure (scenario generator) can be synthesised, from which any-horizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulation-based approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification, when a suitable bound on the horizon or an iterative horizon enlargement strategy is defined, as in the spirit of bounded model checking. |
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1939-3520 |
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To appear |
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MCLab @ davi @ ref9527998 |
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191 |
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Melatti, I.; Mari, F.; Mancini, T.; Prodanovic, M.; Tronci, E. |
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Title |
A Two-Layer Near-Optimal Strategy for Substation Constraint Management via Home Batteries |
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2021 |
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IEEE Transactions on Industrial Electronics |
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1-1 |
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Within electrical distribution networks, substation constraints management requires that aggregated power demand from residential users is kept within suitable bounds. Efficiency of substation constraints management can be measured as the reduction of constraints violations w.r.t. unmanaged demand. Home batteries hold the promise of enabling efficient and user-oblivious substation constraints management. Centralized control of home batteries would achieve optimal efficiency. However, it is hardly acceptable by users, since service providers (e.g., utilities or aggregators) would directly control batteries at user premises. Unfortunately, devising efficient hierarchical control strategies, thus overcoming the above problem, is far from easy. We present a novel two-layer control strategy for home batteries that avoids direct control of home devices by the service provider and at the same time yields near-optimal substation constraints management efficiency. Our simulation results on field data from 62 households in Denmark show that the substation constraints management efficiency achieved with our approach is at least 82% of the one obtained with a theoretical optimal centralized strategy. |
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MCLab @ davi @ ref9513535 |
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190 |
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Author |
Driouich, Y.; Parente, M.; Tronci, E. |
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Modeling cyber-physical systems for automatic verification |
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2017 |
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14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD 2017) |
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1-4 |
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cyber-physical systems;formal verification;maximum power point trackers;power engineering computing;Modelica;automatic verification;complex power electronics systems;cyber-physical systems modeling;distributed maximum power point tracking system;open standard modeling language;Computational modeling;Control systems;Integrated circuit modeling;Mathematical model;Maximum power point trackers;Object oriented modeling;Radiation effects;Automatic Formal Verification;Cyber-Physical Systems;DMPPT;Modeling;Photovoltaic systems;Simulation;System Analysis and Design |
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MCLab @ davi @ ref7981621 |
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