2014 - Fellow of the International Federation of Automatic Control (IFAC)
Thomas Parisini mostly deals with Control theory, Fault detection and isolation, Fault, Nonlinear system and Artificial neural network. Control theory and Multi-agent system are frequently intertwined in his study. The various areas that Thomas Parisini examines in his Fault detection and isolation study include Control engineering and Approximation theory.
His study in Fault is interdisciplinary in nature, drawing from both Fault tolerance, Distributed computing, Isolation and Key. His Nonlinear system research incorporates elements of State variable and Residual. His Radial basis function study, which is part of a larger body of work in Artificial neural network, is frequently linked to Field, bridging the gap between disciplines.
His primary areas of investigation include Control theory, Nonlinear system, Fault detection and isolation, Fault and Artificial neural network. His studies deal with areas such as Bounded function and Feedforward neural network as well as Control theory. His work carried out in the field of Nonlinear system brings together such families of science as Stability, Function, State and Residual.
The study incorporates disciplines such as Control engineering, Algorithm and Control reconfiguration in addition to Fault detection and isolation. His Fault course of study focuses on Fault tolerance and Multi-agent system. His Artificial neural network research is multidisciplinary, relying on both Stochastic approximation and Actuator.
The scientist’s investigation covers issues in Control theory, Robustness, Nonlinear system, Distributed computing and Applied mathematics. The concepts of his Control theory study are interwoven with issues in Telecommunications network and Control. His work in Robustness covers topics such as Range which are related to areas like Platoon.
Thomas Parisini has researched Nonlinear system in several fields, including Real-time computing, Filter and Detector. His work investigates the relationship between Distributed computing and topics such as Fault detection and isolation that intersect with problems in Control reconfiguration. His work deals with themes such as Optimization problem, Nonlinear programming and Kernel, which intersect with Applied mathematics.
His primary areas of study are Robustness, Control theory, Distributed computing, Amplitude and State observer. His Robustness research incorporates themes from Feedback loop, Mobile agent, Open-loop controller and Transient. His research in Control theory intersects with topics in Range, Phase and Record locking.
His Distributed computing study incorporates themes from Observer, Fault, Fault detection and isolation and Cyber-physical system. His research investigates the connection with Fault and areas like Adaptive learning which intersect with concerns in Fault tolerance, Stability and Nonlinear system. His Fault detection and isolation research is multidisciplinary, incorporating perspectives in Control reconfiguration, Residual, Kalman filter, Upper and lower bounds and Computation.
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A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems
Xiaodong Zhang;M.M. Polycarpou;T. Parisini.
IEEE Transactions on Automatic Control (2002)
Adaptive fault-tolerant control of nonlinear uncertain systems: an information-based diagnostic approach
Xiaodong Zhang;T. Parisini;M.M. Polycarpou.
IEEE Transactions on Automatic Control (2004)
Fault diagnosis of a class of nonlinear uncertain systems with Lipschitz nonlinearities using adaptive estimation
Xiaodong Zhang;Marios M. Polycarpou;Thomas Parisini.
A receding-horizon regulator for nonlinear systems and a neural approximation
T. Parisini;R. Zoppoli.
Distributed Fault Detection and Isolation of Large-Scale Discrete-Time Nonlinear Systems: An Adaptive Approximation Approach
R. M. G. Ferrari;T. Parisini;M. M. Polycarpou.
IEEE Transactions on Automatic Control (2012)
Cooperative Constrained Control of Distributed Agents With Nonlinear Dynamics and Delayed Information Exchange: A Stabilizing Receding-Horizon Approach
E. Franco;L. Magni;T. Parisini;M.M. Polycarpou.
IEEE Transactions on Automatic Control (2008)
Sensor bias fault isolation in a class of nonlinear systems
Xiaodong Zhang;T. Parisini;M.M. Polycarpou.
IEEE Transactions on Automatic Control (2005)
Approximating networks and extended Ritz method for the solution of functional optimization problems
R. Zoppoli;M. Sanguineti;T. Parisini.
Journal of Optimization Theory and Applications (2002)
Networked Predictive Control of Uncertain Constrained Nonlinear Systems: Recursive Feasibility and Input-to-State Stability Analysis
Gilberto Pin;T Parisini.
IEEE Transactions on Automatic Control (2011)
Automatic classification of field-collected dinoflagellates by artificial neural network
Marine Ecology Progress Series (1996)
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