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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.
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 StyleGianfranco 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 StyleGianfranco 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.
This paper presents the design and validation of a model-based
Gianfranco Gagliardi; Marco Lupia; Gianni Cario; Alessandro Casavola. Optimal H∞ Control for Lateral Dynamics of Autonomous Vehicles. Sensors 2021, 21, 4072 .
AMA StyleGianfranco Gagliardi, Marco Lupia, Gianni Cario, Alessandro Casavola. Optimal H∞ Control for Lateral Dynamics of Autonomous Vehicles. Sensors. 2021; 21 (12):4072.
Chicago/Turabian StyleGianfranco Gagliardi; Marco Lupia; Gianni Cario; Alessandro Casavola. 2021. "Optimal H∞ Control for Lateral Dynamics of Autonomous Vehicles." Sensors 21, no. 12: 4072.
In underwater localization systems several sources of error may impact in different ways the accuracy of the final position estimates. Through simulations and statistical analysis it is possible to identify and characterize such sources of error and their relative importance. This is especially of use when an accurate localization system has to be designed within required accuracy prescriptions. This approach allows one to also investigate how much these sources of error influence the final position estimates achieved by an Extended Kalman Filter (EKF). This paper presents the results of experiments designed in a virtual environment used to simulate real acoustic underwater localization systems. The paper intends to analyze the main parameters that significantly influence the position estimates achieved by a Short Baseline (SBL) system. Specifically, the results of this analysis are presented for a proprietary localization system constituted by a surface platform equipped with four acoustic transducers used for the localization of an underwater target. The simulator here presented has the purpose of simulating the hardware system and modifying some of its design parameters, such as the base-line length and the errors on the GPS and Inertial Measurement Unit (IMU) units, in order to understand which parameters have to modify for improving the accuracy of the entire positioning system. It is shown that statistical analysis techniques can be of help in determining the best values of these parameters that permit to improve the performance of a real hardware system.
Gianni Cario; Alessandro Casavola; Gianfranco Gagliardi; Marco Lupia; Umberto Severino. Accurate Localization in Acoustic Underwater Localization Systems. Sensors 2021, 21, 762 .
AMA StyleGianni Cario, Alessandro Casavola, Gianfranco Gagliardi, Marco Lupia, Umberto Severino. Accurate Localization in Acoustic Underwater Localization Systems. Sensors. 2021; 21 (3):762.
Chicago/Turabian StyleGianni Cario; Alessandro Casavola; Gianfranco Gagliardi; Marco Lupia; Umberto Severino. 2021. "Accurate Localization in Acoustic Underwater Localization Systems." Sensors 21, no. 3: 762.
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.
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 StyleGianfranco 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 StyleGianfranco 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.
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.
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 StyleGianfranco 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 StyleGianfranco 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.
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.
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 StyleGianfranco 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 StyleGianfranco 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.
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.
Gianfranco Gagliardi; Francesco Tedesco; Alessandro Casavola. Turbocharger Rotational Speed Estimation via Acoustic Measurements. IFAC-PapersOnLine 2019, 52, 273 -278.
AMA StyleGianfranco Gagliardi, Francesco Tedesco, Alessandro Casavola. Turbocharger Rotational Speed Estimation via Acoustic Measurements. IFAC-PapersOnLine. 2019; 52 (5):273-278.
Chicago/Turabian StyleGianfranco Gagliardi; Francesco Tedesco; Alessandro Casavola. 2019. "Turbocharger Rotational Speed Estimation via Acoustic Measurements." IFAC-PapersOnLine 52, no. 5: 273-278.
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.
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 StyleGianfranco 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 StyleGianfranco 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.
An application of a recently proposed fault detection and isolation (FDI) design methodology for linear parameter varying (LPV) systems is presented which concerns the robust detection of stator windings faults of an electrical induction motor regulated by a speed controller. On the basis of a detailed nonlinear mathematical model of the motor, it is shown how, based on a judicious convex interpolation of a family of linearized models, a quasi‐linear parameter varying (quasi‐LPV) approximation capable to catch most of the nonlinearities of the model can be achieved and can directly be used for synthesizing LPV‐FDI observers. The design methodology consists in solving a multi‐objective convex linear matrix inequalities optimization problem where disturbance rejection and fault sensitivity are traded‐off in suitable frequency regions. The resulting diagnostic observer is gain‐scheduled and uses a set of motor variables, assumed measurable online, as a scheduling vector. The effectiveness of the LPV‐FDI framework is illustrated in a final numerical example. Copyright © 2014 John Wiley & Sons, Ltd.
Alessandro Casavola; Gianfranco Gagliardi. Fault detection and isolation of electrical induction motors via LPV fault observers: A case study. International Journal of Robust and Nonlinear Control 2014, 25, 627 -648.
AMA StyleAlessandro Casavola, Gianfranco Gagliardi. Fault detection and isolation of electrical induction motors via LPV fault observers: A case study. International Journal of Robust and Nonlinear Control. 2014; 25 (5):627-648.
Chicago/Turabian StyleAlessandro Casavola; Gianfranco Gagliardi. 2014. "Fault detection and isolation of electrical induction motors via LPV fault observers: A case study." International Journal of Robust and Nonlinear Control 25, no. 5: 627-648.
In this paper, we propose a fault detection and isolation filter design method for internal combustion spark ignition engines. Starting from a detailed nonlinear mean‐value mathematical description of the engine, a novel linear parameter varying (LPV) model approximation is derived on the basis of a judicious convex interpolation of a family of linearized models. A filter structure consisting of a bank of LPV observers is considered, each of them in charge of detecting a particular class of faults and exhibiting low sensitivity to all other faults and exogenous inputs. The resulting diagnostic filter is parameter‐dependent in that a set of measurable engine variables is used online to suitably modify the filter gain so as to better take care of system nonlinearities. The quality of the LPV model approximation of the engine and the diagnostic capabilities of the fault detection and isolation architecture are demonstrated by a series of extensive numerical simulations. Copyright © 2013 John Wiley & Sons, Ltd.
Alessandro Casavola; Domenico Famularo; Gianfranco Gagliardi. A linear parameter varying fault detection and isolation method for internal combustion spark ignition engines. International Journal of Robust and Nonlinear Control 2013, 24, 2018 -2034.
AMA StyleAlessandro Casavola, Domenico Famularo, Gianfranco Gagliardi. A linear parameter varying fault detection and isolation method for internal combustion spark ignition engines. International Journal of Robust and Nonlinear Control. 2013; 24 (14):2018-2034.
Chicago/Turabian StyleAlessandro Casavola; Domenico Famularo; Gianfranco Gagliardi. 2013. "A linear parameter varying fault detection and isolation method for internal combustion spark ignition engines." International Journal of Robust and Nonlinear Control 24, no. 14: 2018-2034.
Alessandro Casavola; Gianfranco Gagliardi. Fault Detection and Isolation of Electrical Induction Motors via LPV Fault Observers. IFAC Proceedings Volumes 2012, 45, 800 -805.
AMA StyleAlessandro Casavola, Gianfranco Gagliardi. Fault Detection and Isolation of Electrical Induction Motors via LPV Fault Observers. IFAC Proceedings Volumes. 2012; 45 (20):800-805.
Chicago/Turabian StyleAlessandro Casavola; Gianfranco Gagliardi. 2012. "Fault Detection and Isolation of Electrical Induction Motors via LPV Fault Observers." IFAC Proceedings Volumes 45, no. 20: 800-805.
Gianfranco Gagliardi; Alessandro Casavola; Domenico Famularo. A fault detection and isolation filter design method for Markov jump linear parameter-varying systems. International Journal of Adaptive Control and Signal Processing 2011, 26, 241 -257.
AMA StyleGianfranco Gagliardi, Alessandro Casavola, Domenico Famularo. A fault detection and isolation filter design method for Markov jump linear parameter-varying systems. International Journal of Adaptive Control and Signal Processing. 2011; 26 (3):241-257.
Chicago/Turabian StyleGianfranco Gagliardi; Alessandro Casavola; Domenico Famularo. 2011. "A fault detection and isolation filter design method for Markov jump linear parameter-varying systems." International Journal of Adaptive Control and Signal Processing 26, no. 3: 241-257.
In this paper we propose a Fault Detection and Isolation (FDI) filter design method for Spark Injection Engines. Starting from a detailed nonlinear mean-value representation of the engine, a LPV approximation is obtained based on a judicious convex interpolation of a family of linearized models. A LPV-FDI filter based on a bank of Luenberger observers is synthesized by ensuring guaranteed levels of disturbance rejection and fault detection and isolation. The resulting diagnostic setup is parameter-dependent and uses a set of engine parameters, assumed measurable on-line, as a scheduling vector. The effectiveness of the LPV-FDI framework is illustrated by numerical examples where the diagnostic capabilities of the proposed FDI architecture are proved.
Gianfranco Gagliardi; Alessandro Casavola; Domenico Famularo. A bank of observers based LPV Fault Detection and Isolation method for Spark Injection Engines. 2010 IEEE International Conference on Control Applications 2010, 561 -566.
AMA StyleGianfranco Gagliardi, Alessandro Casavola, Domenico Famularo. A bank of observers based LPV Fault Detection and Isolation method for Spark Injection Engines. 2010 IEEE International Conference on Control Applications. 2010; ():561-566.
Chicago/Turabian StyleGianfranco Gagliardi; Alessandro Casavola; Domenico Famularo. 2010. "A bank of observers based LPV Fault Detection and Isolation method for Spark Injection Engines." 2010 IEEE International Conference on Control Applications , no. : 561-566.
In this paper we propose a Fault Detection and Isolation (FDI) filter design method for Spark Injection Engines. Starting from a detailed nonlinear Mean-value engine mathematical representation, a LPV approximation based on a judicious convex interpolation of a family of linearized models is obtained. An LPV-FDI filter based on the Luenberger observer theory is synthesized by ensuring guaranteed levels of disturbance rejection and fault detection and isolation. The resulting diagnostic filter is parameter-dependent and uses a set of scheduling engine parameters, assumed measurable on-line. The effectiveness of the LPV-FDI framework is illustrated by numerical examples. The obtained LPV approximation is here validated and the diagnostic capabilities of the proposed FDI architecture proved.
Gianfranco Gagliardi; Alessandro Casavola; F. De Cristofaro; Domenico Famularo; Giuseppe Franzè. A LPV Fault Detection and Isolation method for a Spark Injection Engine. Proceedings of the 2010 American Control Conference 2010, 2230 -2235.
AMA StyleGianfranco Gagliardi, Alessandro Casavola, F. De Cristofaro, Domenico Famularo, Giuseppe Franzè. A LPV Fault Detection and Isolation method for a Spark Injection Engine. Proceedings of the 2010 American Control Conference. 2010; ():2230-2235.
Chicago/Turabian StyleGianfranco Gagliardi; Alessandro Casavola; F. De Cristofaro; Domenico Famularo; Giuseppe Franzè. 2010. "A LPV Fault Detection and Isolation method for a Spark Injection Engine." Proceedings of the 2010 American Control Conference , no. : 2230-2235.
In this paper we propose a Fault Detection and Isolation (FDI) filter design method for Spark Injection Engines. Starting from a detailed nonlinear Mean-value engine mathematical representation, a LPV approximation based on a judicious convex interpolation of a family of linearized models is obtained. An LPV-FDI filter based on the Luenberger observer theory is synthesized by ensuring guaranteed levels of disturbance rejection and fault detection and isolation. The resulting diagnostic filter is parameter-dependent and uses a set of scheduling engine parameters, assumed measurable on-line. The effectiveness of the LPV-FDI framework is illustrated by numerical examples. The obtained LPV approximation is here validated and the diagnostic capabilities of the proposed FDI architecture proved.
Gianfranco Gagliardi; Alessandro Casavola; Domenico Famularo; Giuseppe Franzè. A robust time-varying fault detection and isolation method for a Spark Injection Engine. 18th Mediterranean Conference on Control and Automation, MED'10 2010, 1241 -1246.
AMA StyleGianfranco Gagliardi, Alessandro Casavola, Domenico Famularo, Giuseppe Franzè. A robust time-varying fault detection and isolation method for a Spark Injection Engine. 18th Mediterranean Conference on Control and Automation, MED'10. 2010; ():1241-1246.
Chicago/Turabian StyleGianfranco Gagliardi; Alessandro Casavola; Domenico Famularo; Giuseppe Franzè. 2010. "A robust time-varying fault detection and isolation method for a Spark Injection Engine." 18th Mediterranean Conference on Control and Automation, MED'10 , no. : 1241-1246.