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Marco Lupia
Dipartimento di Ingegneria Elettronica, Informatica e Sistemistica (DIMES), Universitá della Calabria, 87036 Rende, CS, Italy

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Journal article
Published: 13 June 2021 in Sensors
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This paper presents the design and validation of a model-based H vehicle lateral controller for autonomous vehicles in a simulation environment. The controller was designed so that the position and orientation tracking errors are minimized and so that the vehicle is able to follow a trajectory computed in real-time by exploiting proper video-processing and lane-detection algorithms. From a computational point of view, the controller is obtained by solving a suitable LMI optimization problem and ensures that the closed-loop system is robust with respect to variations in the vehicle’s longitudinal speed. In order to show the effectiveness of the proposed control strategy, simulations have been undertaken by taking advantage of a co-simulation environment jointly developed in Matlab/Simulink © and Carsim 8 ©. The simulation activity shows that the proposed control approach allows for good control performance to be achieved.

ACS Style

Gianfranco Gagliardi; Marco Lupia; Gianni Cario; Alessandro Casavola. Optimal H Control for Lateral Dynamics of Autonomous Vehicles. Sensors 2021, 21, 4072 .

AMA Style

Gianfranco Gagliardi, Marco Lupia, Gianni Cario, Alessandro Casavola. Optimal H Control for Lateral Dynamics of Autonomous Vehicles. Sensors. 2021; 21 (12):4072.

Chicago/Turabian Style

Gianfranco Gagliardi; Marco Lupia; Gianni Cario; Alessandro Casavola. 2021. "Optimal H Control for Lateral Dynamics of Autonomous Vehicles." Sensors 21, no. 12: 4072.

Journal article
Published: 23 January 2021 in Sensors
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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.

ACS Style

Gianni Cario; Alessandro Casavola; Gianfranco Gagliardi; Marco Lupia; Umberto Severino. Accurate Localization in Acoustic Underwater Localization Systems. Sensors 2021, 21, 762 .

AMA Style

Gianni Cario, Alessandro Casavola, Gianfranco Gagliardi, Marco Lupia, Umberto Severino. Accurate Localization in Acoustic Underwater Localization Systems. Sensors. 2021; 21 (3):762.

Chicago/Turabian Style

Gianni Cario; Alessandro Casavola; Gianfranco Gagliardi; Marco Lupia; Umberto Severino. 2021. "Accurate Localization in Acoustic Underwater Localization Systems." Sensors 21, no. 3: 762.

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.