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Francesco Tedesco
Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica (DIMES), Università della Calabria, Via Pietro Bucci, Cubo 42-c, Rende (CS), 87036, Italy

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Journal article
Published: 28 June 2021 in International Journal of Control
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This paper presents some outcomes of a recently completed research project aimed at developing torque control strategies for automotive turbocharged combustion engines with modern control design methodologies. Traditional torque control consists of maintaining some relevant signals close to certain set-points generated by a map representing the static inverse model of the engine, without any consideration for the optimality of the responses. The proposed model-based control strategy does not make use of any static map and all signals of interests are regulated at the same time by a unique centralised multivariable controller. A Linear Parameter Varying (LPV) H∞ optimal control design problem, formulated via Linear Matrix Inequality (LMI) feasibility conditions, is solved to generate the controller, whose main objectives are the reduction of fuel consumption while maintaining good torque tracking. The resulting regulator presents is gain-scheduled and is designed to be calibratable in real-time. Some numerical simulations demonstrate the effectiveness of the presented approach.

ACS Style

Gianfranco Gagliardi; Francesco Tedesco; Alessandro Casavola. H∞ Calibratable LPV Control Strategies for Torque Control in Automotive Turbocharged Engines. International Journal of Control 2021, 1 -33.

AMA Style

Gianfranco Gagliardi, Francesco Tedesco, Alessandro Casavola. H∞ Calibratable LPV Control Strategies for Torque Control in Automotive Turbocharged Engines. International Journal of Control. 2021; ():1-33.

Chicago/Turabian Style

Gianfranco Gagliardi; Francesco Tedesco; Alessandro Casavola. 2021. "H∞ Calibratable LPV Control Strategies for Torque Control in Automotive Turbocharged Engines." International Journal of Control , no. : 1-33.

Research article
Published: 21 June 2021 in Transactions of the Institute of Measurement and Control
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In this paper, a resilient distributed control scheme against covert attacks for multi-agent networked systems subject to input and state constraints is developed. The idea consists in a clever deployment of predictive arguments with a twofold aim: detection of malicious agent behaviors affecting the normal system operations and consequent specific control actions implementation to mitigate as much as possible undesirable knock-on effects resulting from adversary actions. Specifically, the multi-agent system is organized in terms of a grid topology and set-theoretic receding horizon control ideas are exploited to develop a distributed algorithm capable to recognize the attacked agent. In essence, the resulting solution relies on the combined use of predictive control and set-invariance ideas that are exploited to generate redundant control sequences randomly selected on the actuator side such that the malicious agent is never aware about the effective control action indeed exploited. As a consequence, countermeasures on the sensor-to-controller channel could lead to significantly erroneous data not complying with the expected evolution of the system modeling. Finally, numerical simulations are carried out to show benefits and effectiveness of the proposed approach.

ACS Style

Francesco Tedesco; Domenico Famularo; Giuseppe Franzè. A resilient control strategy for networked multi-agent systems subject to covert attacks. Transactions of the Institute of Measurement and Control 2021, 1 .

AMA Style

Francesco Tedesco, Domenico Famularo, Giuseppe Franzè. A resilient control strategy for networked multi-agent systems subject to covert attacks. Transactions of the Institute of Measurement and Control. 2021; ():1.

Chicago/Turabian Style

Francesco Tedesco; Domenico Famularo; Giuseppe Franzè. 2021. "A resilient control strategy for networked multi-agent systems subject to covert attacks." Transactions of the Institute of Measurement and Control , no. : 1.

Journal article
Published: 07 December 2020 in Smart Cities
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This paper reports the results of a recently concluded R&D project, SCALS (Smart Cities Adaptive Lighting System), which aimed at the development of all hardware/software components of an adaptive urban smart lighting architecture allowing municipalities to manage and control public street lighting lamps. The system is capable to autonomously adjust street lamps’ brightness on the basis of the presence of vehicles (busses/trucks, cars, motorcycles and bikes) and/or pedestrians in specific areas or segments of the streets/roads of interest to reduce the energy consumption. The main contribution of this work is to design a low cost smart lighting system and, at same time, to define an IoT infrastructure where each lighting pole is an element of a network that can increase their amplitude. More generally, the proposed smart infrastructure can be viewed as the basis of a wider technological architecture aimed at offering value-added services for sustainable cities. The smart architecture combines various sub-systems (local controllers, motion sensors, video-cameras, weather sensors) and electronic devices, each of them in charge of performing specific operations: remote street segments lamp management, single street lamp brightness control, video processing for vehicles motion detection and classification, wireless and wired data exchanges, power consumptions analysis and traffic evaluation. Two pilot sites have been built up in the project where the smart architecture has been tested and validated in real scenarios. Experimental results show that energy savings of up to 80% are possible compared to a traditional street lamp system.

ACS Style

Gianfranco Gagliardi; Marco Lupia; Gianni Cario; Francesco Tedesco; Francesco Cicchello Gaccio; Fabrizio Lo Scudo; Alessandro Casavola. Advanced Adaptive Street Lighting Systems for Smart Cities. Smart Cities 2020, 3, 1495 -1512.

AMA Style

Gianfranco Gagliardi, Marco Lupia, Gianni Cario, Francesco Tedesco, Francesco Cicchello Gaccio, Fabrizio Lo Scudo, Alessandro Casavola. Advanced Adaptive Street Lighting Systems for Smart Cities. Smart Cities. 2020; 3 (4):1495-1512.

Chicago/Turabian Style

Gianfranco Gagliardi; Marco Lupia; Gianni Cario; Francesco Tedesco; Francesco Cicchello Gaccio; Fabrizio Lo Scudo; Alessandro Casavola. 2020. "Advanced Adaptive Street Lighting Systems for Smart Cities." Smart Cities 3, no. 4: 1495-1512.

Journal article
Published: 27 November 2020 in Control Engineering Practice
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An effective Air-to-Fuel Ratio (AFR) control is paramount to ensure a good combustion and high catalyst efficiency. This work addresses the problem of determining continuous-time estimates of AFR in turbocharged Spark Ignition (SI) engines on the basis of binary sparse measurements of the exhaust gas Oxygen. The latter are provided by a HEGO (Heated Exhaust Gas Oxygen) sensor installed at the catalytic converter input in place of a more expensive linear UEGO (Universal Exhaust Gas Oxygen) sensor, as nowadays common in commercial cars. The HEGO sensor outputs two voltage values only, corresponding respectively to low or high concentration of the residual Oxygen in the exhaust gas (on/off behavior). In view of this, it can be classified as a binary sensor generating irregular and sparse measurements in that the useful information is only present at the instants of the on/off and off/on transitions. An estimation scheme based on the use of a recursive least-squares algorithm has been designed by resorting to the theory of linear hybrid observers with quantized inputs. A detailed convergence analysis of the state reconstruction error is also provided. The proposed hybrid observer scheme is employed in a PI control-loop designed to maintain the AFR close to a desired value. The effectiveness of the proposed method is demonstrated by several numerical simulations based on both synthetic and real data.

ACS Style

Gianfranco Gagliardi; Daniele Mari; Francesco Tedesco; Alessandro Casavola. An Air-to-Fuel ratio estimation strategy for turbocharged spark-ignition engines based on sparse binary HEGO sensor measures and hybrid linear observers. Control Engineering Practice 2020, 107, 104694 .

AMA Style

Gianfranco Gagliardi, Daniele Mari, Francesco Tedesco, Alessandro Casavola. An Air-to-Fuel ratio estimation strategy for turbocharged spark-ignition engines based on sparse binary HEGO sensor measures and hybrid linear observers. Control Engineering Practice. 2020; 107 ():104694.

Chicago/Turabian Style

Gianfranco Gagliardi; Daniele Mari; Francesco Tedesco; Alessandro Casavola. 2020. "An Air-to-Fuel ratio estimation strategy for turbocharged spark-ignition engines based on sparse binary HEGO sensor measures and hybrid linear observers." Control Engineering Practice 107, no. : 104694.

Journal article
Published: 20 July 2020 in IEEE Transactions on Control Systems Technology
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Obtaining accurate measures of the turbocharger rotational speed is a key task to achieve good powertrain control performance in turbocharged combustion engines. However, direct access to the rotating parts of a turbocharger requires expensive sensors that present long-term reliability issues. In view of this, this article focuses on the design of measurement architectures for the estimation of the turbocharger shaft rotating speed via the numerical processing of the overall sound emissions acquired by a microphone placed in the vehicle hood. This kind of signal represents an extremely rich source of information about the operating conditions of all noisy powertrain subsystems. The core of the scheme is represented by an adaptive discrete-time nonlinear frequency locked-loop (FLL) filter that is properly designed to extract the useful frequency content from the acquired audio signal. The whole architecture is innovative, flexible, and extremely low cost by requiring, for its implementation, the additional installation of a single microphonic capsule only. Moreover, it exhibits such a modest computational burden to be directly implementable in commercial engine control units (ECUs) without requiring additional computing hardware. Reported experimental assessments show that the accuracy of the estimate is excellent in all allowed rotational speed regimes.

ACS Style

Gianfranco Gagliardi; Francesco Tedesco; Alessandro Casavola. An Adaptive Frequency-Locked-Loop Approach for the Turbocharger Rotational Speed Estimation via Acoustic Measurements. IEEE Transactions on Control Systems Technology 2020, 29, 1437 -1449.

AMA Style

Gianfranco Gagliardi, Francesco Tedesco, Alessandro Casavola. An Adaptive Frequency-Locked-Loop Approach for the Turbocharger Rotational Speed Estimation via Acoustic Measurements. IEEE Transactions on Control Systems Technology. 2020; 29 (4):1437-1449.

Chicago/Turabian Style

Gianfranco Gagliardi; Francesco Tedesco; Alessandro Casavola. 2020. "An Adaptive Frequency-Locked-Loop Approach for the Turbocharger Rotational Speed Estimation via Acoustic Measurements." IEEE Transactions on Control Systems Technology 29, no. 4: 1437-1449.

Journal article
Published: 22 May 2020 in Vibration
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Regenerative suspension systems, unlike traditional passive, semi-active or active setups, are able to convert the traditionally wasted kinetic energy into electricity. This paper discusses flexible multi-objective control design strategies based on LMI formulations to suitably trade-off between the usual road handling and ride comfort performance and the amount of energy to be harvested. An electromechanical regenerative vehicle suspension system is considered where the shock absorber of each wheel is replaced by a linear electrical motor which is actively governed. It is shown by simulations that multivariable centralized control laws designed on the basis of a full-car model of the suspension system are able to achieve larger amount of harvested energy under identical ride comfort prescriptions with respect to scalar decentralized control strategies, designed on the basis of a single quarter-car model and implemented independently on each wheel in a decentralized way. Improvements up to 40 % and 20 % of harvested energy are respectively achievable by the centralized multivariable H 2 and H ∞ optimal controllers under the same test conditions.

ACS Style

Alessandro Casavola; Francesco Tedesco; Pasquale Vaglica. H2 and H Optimal Control Strategies for Energy Harvesting by Regenerative Shock Absorbers in Cars. Vibration 2020, 3, 99 -115.

AMA Style

Alessandro Casavola, Francesco Tedesco, Pasquale Vaglica. H2 and H Optimal Control Strategies for Energy Harvesting by Regenerative Shock Absorbers in Cars. Vibration. 2020; 3 (2):99-115.

Chicago/Turabian Style

Alessandro Casavola; Francesco Tedesco; Pasquale Vaglica. 2020. "H2 and H Optimal Control Strategies for Energy Harvesting by Regenerative Shock Absorbers in Cars." Vibration 3, no. 2: 99-115.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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In this paper a supervisory strategy for load/frequency control problems in networked multi-area electrical micro-grids in the presence of Renewable Energy Systems (RES) is presented. The proposed strategy exploits a recently developed constrained supervision methodology known in the literature as the Reference-Offset Governor (ROG) approach. Here, the ROG approach is extended to operate in the presence of rate-bounded disturbances acting as non-manipulable inputs on the plant. The main aim is at adequately orchestrating, during the on-line operations, the switching among different ROG configurations, suitably calibrated on the intensity of the disturbances, to efficiently satisfy the prescribed constraints. It is shown that the use of a bank of ROGs, instead of a single one, can remarkably reduce the conservativeness of the solution and improve the overall performance if the disturbance intensity changes. The effectiveness of the proposed approach is demonstrated on a two-area power system subject to coordination constraints on maximum frequency deviations, exchanged and generated powers and injected power from local RESs.

ACS Style

Francesco Tedesco; Alessandro Casavola. Load/Frequency Control in the presence of Renewable Energy Systems: a Reference-Offset Governor approach. IFAC-PapersOnLine 2020, 53, 12548 -12553.

AMA Style

Francesco Tedesco, Alessandro Casavola. Load/Frequency Control in the presence of Renewable Energy Systems: a Reference-Offset Governor approach. IFAC-PapersOnLine. 2020; 53 (2):12548-12553.

Chicago/Turabian Style

Francesco Tedesco; Alessandro Casavola. 2020. "Load/Frequency Control in the presence of Renewable Energy Systems: a Reference-Offset Governor approach." IFAC-PapersOnLine 53, no. 2: 12548-12553.

Journal article
Published: 19 September 2019 in IFAC-PapersOnLine
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This paper deals with the estimation of the turbocharger rotating speed via the numerical processing of the overall sound emissions acquired via a microphone placed under the vehicle hood. As a matter of fact, this kind of signals represents an extremely rich information source about the operating conditions of all the noisy powertrain subsystems. The core of the scheme is represented by an adaptive Frequency Locked-Loop (FLL) filter that is properly designed so as to extract useful frequency content from the acquired audio signals. The whole architecture, requiring the use of a single microphone only, can be considered innovative and low-cost for automotive applications. Experimental outcomes demonstrate that the approach is ready to be introduced in the Engine Control Unit (ECU) in order to implement suitable strategies for turbocharger control.

ACS Style

Gianfranco Gagliardi; Francesco Tedesco; Alessandro Casavola. Turbocharger Rotational Speed Estimation via Acoustic Measurements. IFAC-PapersOnLine 2019, 52, 273 -278.

AMA Style

Gianfranco Gagliardi, Francesco Tedesco, Alessandro Casavola. Turbocharger Rotational Speed Estimation via Acoustic Measurements. IFAC-PapersOnLine. 2019; 52 (5):273-278.

Chicago/Turabian Style

Gianfranco Gagliardi; Francesco Tedesco; Alessandro Casavola. 2019. "Turbocharger Rotational Speed Estimation via Acoustic Measurements." IFAC-PapersOnLine 52, no. 5: 273-278.

Conference paper
Published: 01 July 2019 in 2019 27th Mediterranean Conference on Control and Automation (MED)
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The Air-To-Fuel ratio (AFR) is a crucial parameter to ensure good quality combustion and high catalyst efficiency. This work addresses the problem of determining continuous-time estimates of AFR in turbocharged Spark Ignition (SI) engines on the basis of only binary (on/off) sparse measurements of the exhaust gas Oxygen determined by using a HEGO (Heated Exhaust Gas Oxygen) sensor placed at the catalytic converter input in place of a more expensive linear UEGO (Universal Exhaust Gas Oxygen) sensor, as nowadays standard in commercial cars. In this context, the HEGO sensor, providing at its output terminal only two voltage values, can be considered a binary sensor with irregular and sparse measurements in that the useful information in only present at the instants of the on/off and off/on transitions. The theory of linear hybrid observers with quantized outputs is used to single out an estimation scheme based on a recursive least-squares algorithm. A detailed convergence analysis of the state reconstruction error is also provided. The proposed hybrid observer scheme is then used in a PI control loop finalized to maintain the AFR close to a desired value. The effectiveness of the proposed method is demonstrated by numerical simulations.

ACS Style

Gianfranco Gagliardi; Daniele Mari; Francesco Tedesco; Alessandro Casavola. Air-to-Fuel Ratio Estimation in Turbocharged Spark-Ignition Engines based on Binary HEGO Sensors. 2019 27th Mediterranean Conference on Control and Automation (MED) 2019, 374 -379.

AMA Style

Gianfranco Gagliardi, Daniele Mari, Francesco Tedesco, Alessandro Casavola. Air-to-Fuel Ratio Estimation in Turbocharged Spark-Ignition Engines based on Binary HEGO Sensors. 2019 27th Mediterranean Conference on Control and Automation (MED). 2019; ():374-379.

Chicago/Turabian Style

Gianfranco Gagliardi; Daniele Mari; Francesco Tedesco; Alessandro Casavola. 2019. "Air-to-Fuel Ratio Estimation in Turbocharged Spark-Ignition Engines based on Binary HEGO Sensors." 2019 27th Mediterranean Conference on Control and Automation (MED) , no. : 374-379.

Journal article
Published: 03 June 2019 in IEEE Control Systems Letters
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In this paper, a resilient control strategy against replay attacks is developed for discrete-time linear systems subject to state and input constraints, bounded disturbances and measurement noises. In particular operating scenarios, where adversaries act on the communication network by maliciously repeating data transmitted from the sensor to the controller, are investigated. The idea is to customize basic model predictive control schemes for detection attack and resilient control action purposes by exploiting set-theoretic and feasibility arguments proper of the receding control horizon philosophy.

ACS Style

Giuseppe Franze; Francesco Tedesco; Walter Lucia. Resilient Control for Cyber-Physical Systems Subject to Replay Attacks. IEEE Control Systems Letters 2019, 3, 984 -989.

AMA Style

Giuseppe Franze, Francesco Tedesco, Walter Lucia. Resilient Control for Cyber-Physical Systems Subject to Replay Attacks. IEEE Control Systems Letters. 2019; 3 (4):984-989.

Chicago/Turabian Style

Giuseppe Franze; Francesco Tedesco; Walter Lucia. 2019. "Resilient Control for Cyber-Physical Systems Subject to Replay Attacks." IEEE Control Systems Letters 3, no. 4: 984-989.

Conference paper
Published: 01 April 2019 in 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT)
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In this paper, a novel control architecture capable of managing denial-of-service attacks affecting the communication links between a group of interconnected systems and remote controllers is presented. The basic idea relies on the representation of the interconnected cyber-physical system as a leader-follower configuration so that adequate control actions are computed in order to isolate the attacked unit that otherwise could compromise system operations. Simulations on a multiarea power system confirm that the proposed control scheme can reconfigure the leader-follower structure in response to denial-of-service (DoS) attacks occurring on both sensor-to-controller and controller-to-actuator channels.

ACS Style

Giuseppe Franze; Walter Lucia; Francesco Tedesco. A Leader-Follower Set-theoretic Approach for Cyber-Physical Systems against Denial-of-Service Attacks. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT) 2019, 73 -78.

AMA Style

Giuseppe Franze, Walter Lucia, Francesco Tedesco. A Leader-Follower Set-theoretic Approach for Cyber-Physical Systems against Denial-of-Service Attacks. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). 2019; ():73-78.

Chicago/Turabian Style

Giuseppe Franze; Walter Lucia; Francesco Tedesco. 2019. "A Leader-Follower Set-theoretic Approach for Cyber-Physical Systems against Denial-of-Service Attacks." 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT) , no. : 73-78.

Journal article
Published: 11 October 2018 in IFAC-PapersOnLine
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This paper presents a fault-tolerant sensor reconciliation design approach for over-sensed plants (see Fig. 1). The reconciliator is in charge of detecting, at each time instant, the possibly faulty physical sensors y and generating a virtual output z (with dimy ≥ dimz) where the corrupted measures coming from the pool of redundant physical output are removed. In this way the virtual output is always healthy and usable for control purposes without requiring the reconfiguration of the nominal control law. The approach is based on the use of Unknown Input Observers (UIO) with Linear Fractional Transformation (LFT) parameter dependency and works together with an "ad-hoc" parameters estimator that is designed to estimate on-line at each time instant the sensor effectiveness matrix. The sensor faults here considered are limited to variation of sensors’ gain and offset values. All main properties of the scheme are investigated and rigorously proved. A final simulation example is included to show the effectiveness of the proposed scheme.

ACS Style

Hamid Behzad; Alessandro Casavola; Francesco Tedesco; Mohammad Ali Sadrnia. Fault-Tolerant Sensor Reconciliation Schemes via LFT Unknown Input Observers. IFAC-PapersOnLine 2018, 51, 874 -879.

AMA Style

Hamid Behzad, Alessandro Casavola, Francesco Tedesco, Mohammad Ali Sadrnia. Fault-Tolerant Sensor Reconciliation Schemes via LFT Unknown Input Observers. IFAC-PapersOnLine. 2018; 51 (24):874-879.

Chicago/Turabian Style

Hamid Behzad; Alessandro Casavola; Francesco Tedesco; Mohammad Ali Sadrnia. 2018. "Fault-Tolerant Sensor Reconciliation Schemes via LFT Unknown Input Observers." IFAC-PapersOnLine 51, no. 24: 874-879.

Journal article
Published: 11 October 2018 in IFAC-PapersOnLine
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In this paper, we propose a distributed sensor selection architecture for a class of networked systems characterized by three distinct groups of nodes: plants, sensors and computational agents. We address the problem of fault tolerant state estimation by fusing information on the plants provided by possibly faulty sensors via a formal exploitation of trustworthiness concepts. Specifically, the scheme uses the opinions (trust) that agents have on the quality of the measurements to select the most suitable sensors for state estimation purposes. The trust is evaluated via a novel and original adaptive reputation mechanism that is in charge of intercepting those sensors that minimize an ad-hoc criterion on the degradation level of the state measurements. Finally, it is formally proved that the resulting estimation scheme is always capable to select the healthy sensor in finite-time. Some numerical simulations instrumental to show the capability of the proposed reputation mechanism to efficiently select healthy sensors from a given set are finally reported.

ACS Style

Francesco Tedesco; Giuseppe Franzè; Alessandro Casavola. Sensor selection schemes for fault tolerant state estimation via sensor trustworthiness. IFAC-PapersOnLine 2018, 51, 880 -885.

AMA Style

Francesco Tedesco, Giuseppe Franzè, Alessandro Casavola. Sensor selection schemes for fault tolerant state estimation via sensor trustworthiness. IFAC-PapersOnLine. 2018; 51 (24):880-885.

Chicago/Turabian Style

Francesco Tedesco; Giuseppe Franzè; Alessandro Casavola. 2018. "Sensor selection schemes for fault tolerant state estimation via sensor trustworthiness." IFAC-PapersOnLine 51, no. 24: 880-885.

Review article
Published: 18 July 2018 in Journal of the Franklin Institute
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This paper illustrates the derivation of a linear parameter varying (LPV) model approximation of a turbocharged Spark-Ignition (SI) automotive engine and its usage in designing a model-based fault detection and isolation (FDI) scheme. The LPV approximation is derived from a detailed nonlinear mathematical model of the engine on the basis of the well known Jacobian approach. The resulting LPV representation is then exploited for synthesizing a bank of LPV-FDI H∞/H− Luenberger observers. Each observer is in charge of detecting a particular class of fault and is designed for having low sensitivity to all other exogenous inputs so as to allow an effective fault isolation. The adopted FDI scheme is gain-scheduled and exploits a set of engine variables, assumed to be measurable on-line, as a scheduling parameters. The goodness of the LPV approximation of the engine model and the effectiveness of the LPV-FDI architecture are demonstrated by several numerical simulations.

ACS Style

Gianfranco Gagliardi; Francesco Tedesco; Alessandro Casavola. A LPV modeling of turbocharged spark-ignition automotive engine oriented to fault detection and isolation purposes. Journal of the Franklin Institute 2018, 355, 6710 -6745.

AMA Style

Gianfranco Gagliardi, Francesco Tedesco, Alessandro Casavola. A LPV modeling of turbocharged spark-ignition automotive engine oriented to fault detection and isolation purposes. Journal of the Franklin Institute. 2018; 355 (14):6710-6745.

Chicago/Turabian Style

Gianfranco Gagliardi; Francesco Tedesco; Alessandro Casavola. 2018. "A LPV modeling of turbocharged spark-ignition automotive engine oriented to fault detection and isolation purposes." Journal of the Franklin Institute 355, no. 14: 6710-6745.

Original articles
Published: 13 July 2018 in International Journal of Control
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This paper proposes two fault-tolerant sensor reconciliation design methods for over-sensed plants. The aim of the reconciliator is to detect the presence of faults on the existing physical sensors and hide the corrupted measurements in the generation of a virtual output, which one would like to be generated in a reliable way in spite of fault occurrences and hence trustfully usable for control purposes. The sensor faults here considered are limited to variations of both sensor gain and offset values. The proposed approach envisages the use of an unknown input observers coupled with an ‘ad-hoc’ parameters estimator used to estimate online the sensor effectiveness matrix at each time instant. In the paper, two design methodologies are described, based one on the linear parameter varying polytopic formulation and the other on the linear fractional transformation paradigm. All main properties of the considered schemes are investigated and rigorously proved.

ACS Style

Hamid Behzad; Alessandro Casavola; Francesco Tedesco; Mohammad Ali Sadrnia. Fault-tolerant sensor reconciliation schemes based on unknown input observers. International Journal of Control 2018, 93, 669 -679.

AMA Style

Hamid Behzad, Alessandro Casavola, Francesco Tedesco, Mohammad Ali Sadrnia. Fault-tolerant sensor reconciliation schemes based on unknown input observers. International Journal of Control. 2018; 93 (3):669-679.

Chicago/Turabian Style

Hamid Behzad; Alessandro Casavola; Francesco Tedesco; Mohammad Ali Sadrnia. 2018. "Fault-tolerant sensor reconciliation schemes based on unknown input observers." International Journal of Control 93, no. 3: 669-679.

Conference paper
Published: 01 June 2018 in 2018 European Control Conference (ECC)
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ACS Style

Alessandro Casavola; Emanuele Garone; Francesco Tedesco. On the Link Between Multi-Coloring Problems for Graphs and Distributed Supervision of Interconnected Systems. 2018 European Control Conference (ECC) 2018, 1 .

AMA Style

Alessandro Casavola, Emanuele Garone, Francesco Tedesco. On the Link Between Multi-Coloring Problems for Graphs and Distributed Supervision of Interconnected Systems. 2018 European Control Conference (ECC). 2018; ():1.

Chicago/Turabian Style

Alessandro Casavola; Emanuele Garone; Francesco Tedesco. 2018. "On the Link Between Multi-Coloring Problems for Graphs and Distributed Supervision of Interconnected Systems." 2018 European Control Conference (ECC) , no. : 1.

Conference paper
Published: 01 May 2018 in Electrical Engineering (ICEE), Iranian Conference on
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This paper presents a fault-tolerant sensor reconciliation scheme for systems subject to sensor's gain and offset faults. The reconciliator is in charge of fault accommodation without requiring the reconfiguration of the nominal control law. Amongst many, the solution proposed here is based on the use of a Linear Parameter Varying Unknown Input Observers (LPV-UIO) that exploit a Taylor linearization to arrive to a convex optimization formulation for the observer gain design. The LPV-UIO observer is therefore used to hide possibly faulty measures from the pool of physical outputs in the generation of arguably healthy virtual outputs to be used for control purposes. The scheme is fully described and all of its properties investigated and proved. Finally, a simulation example is reported in details to show the effectiveness of the scheme.

ACS Style

Hamid Behzad; Alessandro Casavola; Francesco Tedesco; Mohammad Ali Sadrnia. Use of LPV-LFT Unknown Input Observers for the Design of Fault Tolerant Sensor Reconciliation Schemes. Electrical Engineering (ICEE), Iranian Conference on 2018, 899 -905.

AMA Style

Hamid Behzad, Alessandro Casavola, Francesco Tedesco, Mohammad Ali Sadrnia. Use of LPV-LFT Unknown Input Observers for the Design of Fault Tolerant Sensor Reconciliation Schemes. Electrical Engineering (ICEE), Iranian Conference on. 2018; ():899-905.

Chicago/Turabian Style

Hamid Behzad; Alessandro Casavola; Francesco Tedesco; Mohammad Ali Sadrnia. 2018. "Use of LPV-LFT Unknown Input Observers for the Design of Fault Tolerant Sensor Reconciliation Schemes." Electrical Engineering (ICEE), Iranian Conference on , no. : 899-905.

Conference paper
Published: 01 April 2018 in 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC)
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This paper presents an urban smart lighting system capable to autonomously control the street lamp lighting level by exploiting data related to vehicles (bus, car, motorcycle and bike) and/or pedestrians traffic in a specific area. The system is able to set the lighting level on the basis of required needs and allows one to reduce energy costs. To this end, it makes use of local controllers, motion sensors, video-camera and electronic devices for video processing. In this way the inputs coming from the sensors is processed in order to optimally dim the lighting by applying an 1-10 V control input voltage to the fixture or driver. The control can be performed either on a decentralized fashion on each single street lamp or on a group of them. Experimental results show that with respect to a traditional street lamp system, the presented architecture allows energy saving up to 65%.

ACS Style

Gianfranco Gagliardi; Alessandro Casavola; Marco Lupia; Gianni Cario; Francesco Tedesco; Fabrizio Lo Scudo; Francesco Cicchello Gaccio; Antonio Augimeri. A smart city adaptive lighting system. 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC) 2018, 258 -263.

AMA Style

Gianfranco Gagliardi, Alessandro Casavola, Marco Lupia, Gianni Cario, Francesco Tedesco, Fabrizio Lo Scudo, Francesco Cicchello Gaccio, Antonio Augimeri. A smart city adaptive lighting system. 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC). 2018; ():258-263.

Chicago/Turabian Style

Gianfranco Gagliardi; Alessandro Casavola; Marco Lupia; Gianni Cario; Francesco Tedesco; Fabrizio Lo Scudo; Francesco Cicchello Gaccio; Antonio Augimeri. 2018. "A smart city adaptive lighting system." 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC) , no. : 258-263.

Article
Published: 20 February 2018 in International Journal of Robust and Nonlinear Control
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In this paper, a distributed command governor (CG) strategy is introduced that, by the use of graph colorability theory, improves the scalability property and the performance of recently introduced distributed noncooperative sequential CG strategies. The latter are characterized by the fact that only 1 agent at a decision time is allowed to update its command, whereas all the others keep applying their previously computed commands. The scalability of these early CG distributed schemes and their performance are limited because the structure of the constraints is not taken into account in their implementation. Here, by exploiting the idea that agents that are not directly coupled by the constraints can simultaneously update their control actions, the agents in the network are grouped into particular subsets (turns). At each time instant, on the basis of a round-robin policy, all agents belonging to a turn are allowed to update simultaneously their commands, whereas agents in other turns keep applying their previous commands. Then, a turn-based distributed CG strategy is proposed and its main properties are analyzed. Graph colorability theory is used to determine the minimal number of turns and to distribute each agent in at least a turn. A novel graph colorability problem that allows one to maximize the frequency at which agents can update their commands is proposed and discussed. A final example is presented to illustrate the effectiveness of the proposed strategy.

ACS Style

Alessandro Casavola; Emanuele Garone; Francesco Tedesco. A distributed command governor based on graph colorability theory. International Journal of Robust and Nonlinear Control 2018, 28, 3056 -3072.

AMA Style

Alessandro Casavola, Emanuele Garone, Francesco Tedesco. A distributed command governor based on graph colorability theory. International Journal of Robust and Nonlinear Control. 2018; 28 (8):3056-3072.

Chicago/Turabian Style

Alessandro Casavola; Emanuele Garone; Francesco Tedesco. 2018. "A distributed command governor based on graph colorability theory." International Journal of Robust and Nonlinear Control 28, no. 8: 3056-3072.

Conference paper
Published: 01 January 2018 in Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics
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ACS Style

Hamid Behzad; Alessandro Casavola; Francesco Tedesco; Mohammad Ali Sadrnia; Gianfranco Gagliardi. A Fault-Tolerant Sensor Reconciliation Scheme based on Self-Tuning LPV Observers. Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics 2018, 1 .

AMA Style

Hamid Behzad, Alessandro Casavola, Francesco Tedesco, Mohammad Ali Sadrnia, Gianfranco Gagliardi. A Fault-Tolerant Sensor Reconciliation Scheme based on Self-Tuning LPV Observers. Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics. 2018; ():1.

Chicago/Turabian Style

Hamid Behzad; Alessandro Casavola; Francesco Tedesco; Mohammad Ali Sadrnia; Gianfranco Gagliardi. 2018. "A Fault-Tolerant Sensor Reconciliation Scheme based on Self-Tuning LPV Observers." Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics , no. : 1.