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B. P. Hayes, I. Melatti, T. Mancini, M. Prodanovic, and E. Tronci. "Residential Demand Management using Individualised Demand Aware Price Policies." IEEE Transactions On Smart Grid 8, no. 3 (2017): 1284–1294. DOI: 10.1109/TSG.2016.2596790.
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Toni Mancini, Federico Mari, Annalisa Massini, Igor Melatti, and Enrico Tronci. "Simulator Semantics for System Level Formal Verification." In Proceedings Sixth International Symposium on Games, Automata, Logics and Formal Verification (GandALF 2015),., 2015. DOI: 10.4204/EPTCS.193.7.
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Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. "Model Based Synthesis of Control Software from System Level Formal Specifications." ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 23, no. 1 (2014): Article 6. ACM. ISSN: 1049-331X. DOI: 10.1145/2559934.
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Giuseppe Della Penna, Daniele Magazzeni, Alberto Tofani, Benedetto Intrigila, Igor Melatti, and Enrico Tronci. "Automated Generation of Optimal Controllers through Model Checking Techniques." In Icinco-Icso, edited by J. Andrade-Cetto, J. - L. Ferrier, J. M. C. D. Pereira and J. Filipe, 26–33. INSTICC Press, 2006. ISSN: 972-8865-59-7. DOI: 10.1007/978-3-540-79142-3.
Abstract: We present a methodology for the synthesis of controllers, which exploits (explicit) model checking techniques. That is, we can cope with the systematic exploration of a very large state space. This methodology can be applied to systems where other approaches fail. In particular, we can consider systems with an highly non-linear dynamics and lacking a uniform mathematical description (model). We can also consider situations where the required control action cannot be specified as a local action, and rather a kind of planning is required. Our methodology individuates first a raw optimal controller, then extends it to obtain a more robust one. A case study is presented which considers the well known truck-trailer obstacle avoidance parking problem, in a parking lot with obstacles on it. The complex non-linear dynamics of the truck-trailer system, within the presence of obstacles, makes the parking problem extremely hard. We show how, by our methodology, we can obtain optimal controllers with different degrees of robustness.
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Giuseppe Della Penna, Alberto Tofani, Marcello Pecorari, Orazio Raparelli, Benedetto Intrigila, Igor Melatti, and Enrico Tronci. "A Case Study on Automated Generation of Integration Tests." In Fdl, 278–284. Ecsi, 2006. ISSN: 978-3-00-019710-9.
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Andrea Bobbio, Sandro Bologna, Michele Minichino, Ester Ciancamerla, Piero Incalcaterra, Corrado Kropp, and Enrico Tronci. "Advanced techniques for safety analysis applied to the gas turbine control system of Icaro co generative plant." In X Convegno Tecnologie e Sistemi Energetici Complessi, 339–350. Genova, Italy, 2001.
Abstract: The paper describes two complementary and integrable approaches, a probabilistic one and a deterministic one, based on classic and advanced modelling techniques for safety analysis of complex computer based systems. The probabilistic approach is based on classical and innovative probabilistic analysis methods. The deterministic approach is based on formal verification methods. Such approaches are applied to the gas turbine control system of ICARO co generative plant, in operation at ENEA CR Casaccia. The main difference between the two approaches, behind the underlining different theories, is that the probabilistic one addresses the control system by itself, as the set of sensors, processing units and actuators, while the deterministic one also includes the behaviour of the equipment under control which interacts with the control system. The final aim of the research, documented in this paper, is to explore an innovative method which put the probabilistic and deterministic approaches in a strong relation to overcome the drawbacks of their isolated, selective and fragmented use which can lead to inconsistencies in the evaluation results.
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Enrico Tronci. "Optimal Finite State Supervisory Control." In CDC '96: Proceedings of the 35th IEEE International Conference on Decision and Control. Washington, DC, USA: IEEE Computer Society, 1996. DOI: 10.1109/CDC.1996.572981.
Abstract: 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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Enrico Tronci. "On Computing Optimal Controllers for Finite State Systems." In CDC '97: Proceedings of the 36th IEEE International Conference on Decision and Control. Washington, DC, USA: IEEE Computer Society, 1997.
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Amedeo Cesta, Alberto Finzi, Simone Fratini, Andrea Orlandini, and Enrico Tronci. "Flexible Plan Verification: Feasibility Results." In 16th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion” (RCRA). Proceedings., 2009.
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Vadim Alimguzhin, Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. A Map-Reduce Parallel Approach to Automatic Synthesis of Control Software. Vol. abs/1210.2276. CoRR, Technical Report, 2012. http://arxiv.org/abs/1210.2276 (accessed May 7, 2024).
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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