Zobrazeno 1 - 10
of 506
pro vyhledávání: '"Spínello P"'
Autor:
Costa, Alessandro, Mastriani, Emilio, Incardona, Federico, Munari, Kevin, Spinello, Sebastiano
This study introduces a predictive maintenance strategy for high pressure industrial compressors using sensor data and features derived from unsupervised clustering integrated into classification models. The goal is to enhance model accuracy and effi
Externí odkaz:
http://arxiv.org/abs/2411.13919
Publikováno v:
Journal of Dynamic Systems, Measurement, and Control. February 2023; 145(2)
We propose a Kullback-Leibler Divergence (KLD) filter to extract anomalies within data series generated by a broad class of proximity sensors, along with the anomaly locations and their relative sizes. The technique applies to devices commonly used i
Externí odkaz:
http://arxiv.org/abs/2405.03047
Publikováno v:
IEEE International Symposium on Robotic and Sensors Environments (ROSE) 2021
The flocking motion control is concerned with managing the possible conflicts between local and team objectives of multi-agent systems. The overall control process guides the agents while monitoring the flock-cohesiveness and localization. The underl
Externí odkaz:
http://arxiv.org/abs/2303.10035
Publikováno v:
IEEE International Symposium on Robotic and Sensors Environments (ROSE) 2021
Model-reference adaptive systems refer to a consortium of techniques that guide plants to track desired reference trajectories. Approaches based on theories like Lyapunov, sliding surfaces, and backstepping are typically employed to advise adaptive c
Externí odkaz:
http://arxiv.org/abs/2303.09994
Publikováno v:
IEEE International Conference on Robotics and Automation (ICRA) 2021
The flock-guidance problem enjoys a challenging structure where multiple optimization objectives are solved simultaneously. This usually necessitates different control approaches to tackle various objectives, such as guidance, collision avoidance, an
Externí odkaz:
http://arxiv.org/abs/2303.09946
Real-Time Measurement-Driven Reinforcement Learning Control Approach for Uncertain Nonlinear Systems
Publikováno v:
Engineering Applications of Artificial Intelligence (Elsevier), vol. 122, June 2023
The paper introduces an interactive machine learning mechanism to process the measurements of an uncertain, nonlinear dynamic process and hence advise an actuation strategy in real-time. For concept demonstration, a trajectory-following optimization
Externí odkaz:
http://arxiv.org/abs/2303.08745
Optical satellite-to-ground communication (OSGC) has the potential to improve access to fast and affordable Internet in remote regions. Atmospheric turbulence, however, distorts the optical beam, eroding the data rate potential when coupling into sin
Externí odkaz:
http://arxiv.org/abs/2303.07516
Publikováno v:
IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, ON, Canada, 2020, pp. 1866-1871
Underactuated systems like sea vessels have degrees of motion that are insufficiently matched by a set of independent actuation forces. In addition, the underlying trajectory-tracking control problems grow in complexity in order to decide the optimal
Externí odkaz:
http://arxiv.org/abs/2104.00190
Guidance Mechanism for Flexible Wing Aircraft Using Measurement-Interfaced Machine Learning Platform
Publikováno v:
IEEE Transactions on Instrumentation and Measurement, Volume 69, Issue 7, 4637-4648 (July 2020)
The autonomous operation of flexible-wing aircraft is technically challenging and has never been presented within literature. The lack of an exact modeling framework is due to the complex nonlinear aerodynamic relationships governed by the deformatio
Externí odkaz:
http://arxiv.org/abs/2103.15945
The optimal tracking problem is addressed in the robotics literature by using a variety of robust and adaptive control approaches. However, these schemes are associated with implementation limitations such as applicability in uncertain dynamical envi
Externí odkaz:
http://arxiv.org/abs/2011.03881