L
Luigi Chisci
Researcher at University of Florence
Publications - 231
Citations - 5723
Luigi Chisci is an academic researcher from University of Florence. The author has contributed to research in topics: Model predictive control & Kalman filter. The author has an hindex of 32, co-authored 217 publications receiving 4507 citations. Previous affiliations of Luigi Chisci include Stanford University & Leonardo.
Papers
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Journal ArticleDOI
Systems with persistent disturbances: predictive control with restricted constraints
TL;DR: Predictive regulation of linear discrete-time systems subject to unknown but bounded disturbances and to state/control constraints and an algorithm based on constraint restrictions is presented and its stability properties are analysed.
Journal ArticleDOI
Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability
Giorgio Battistelli,Luigi Chisci +1 more
TL;DR: This paper addresses distributed state estimation over a sensor network wherein each node-equipped with processing, communication and sensing capabilities-repeatedly fuses local information with information from the neighbors, and derives a novel distributed state estimator.
Journal ArticleDOI
Consensus-Based Linear and Nonlinear Filtering
TL;DR: Novel theoretical results, limitedly to linear systems, on the guaranteed stability of the Hybrid CMCI filters under collective observability and network connectivity are proved.
Journal ArticleDOI
Consensus CPHD Filter for Distributed Multitarget Tracking
TL;DR: A novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density filter is developed that provides a fully distributed, scalable and computationally efficient solution to the distributed multitarget tracking problem.
Journal ArticleDOI
Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines
Luigi Chisci,Antonio Mavino,Guido Perferi,Marco Sciandrone,Carmelo Anile,Gabriella Colicchio,Filomena Fuggetta +6 more
TL;DR: The proposed solution relies in a novel way on autoregressive modeling of the EEG time series and combines a least-squares parameter estimator for EEG feature extraction along with a support vector machine (SVM) for binary classification between preictal/ictal and interictal states.