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Riccardo Scattolini
Researcher at Polytechnic University of Milan
Publications - 312
Citations - 9932
Riccardo Scattolini is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Model predictive control & Nonlinear system. The author has an hindex of 42, co-authored 307 publications receiving 8809 citations. Previous affiliations of Riccardo Scattolini include Polytechnic University of Turin & University of Pavia.
Papers
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Architectures for distributed and hierarchical Model Predictive Control - A review
TL;DR: A classification of a number of decentralized, distributed and hierarchical control architectures for large scale systems is proposed and attention is focused on the design approaches based on Model Predictive Control.
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Distributed model predictive control: A tutorial review and future research directions
TL;DR: The goal is to not only conceptually review the results in this area but also to provide enough algorithmic details so that the advantages and disadvantages of the various approaches can become quite clear.
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A stabilizing model-based predictive control algorithm for nonlinear systems
TL;DR: Using distinct prediction and control horizons, nonlinear model-based predictive control can guarantee: (i) computational efficiency, (ii) enlargement of the stability domain and (iii) local optimality.
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Constrained receding-horizon predictive control
David Clarke,Riccardo Scattolini +1 more
TL;DR: Constrained receding-horizon predictive control (CRHPC) as mentioned in this paper optimizes a quadratic function over a costing horizon subject to the condition that the output matches the reference value over a further constraint range.
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Stochastic linear Model Predictive Control with chance constraints – A review
TL;DR: The main ideas underlying SMPC are presented and different classifications of the available methods are proposed in terms of the dynamic characteristics of the system under control, the performance index to be minimized, the meaning and management of the probabilistic constraints adopted, and their feasibility and convergence properties.