Riccardo Scattolini spends much of his time researching Control theory, Model predictive control, Linear system, Nonlinear system and Nonlinear control. Riccardo Scattolini connects Control theory with Horizon in his research. Riccardo Scattolini has researched Model predictive control in several fields, including Robust control, Robustness, Mathematical optimization, Decentralised system and Process control.
His Linear system research includes elements of Transmission, State variable, Algorithm, Discrete system and Digital control. His work on Nonlinear model as part of general Nonlinear system research is frequently linked to Perturbation, bridging the gap between disciplines. The Nonlinear control study combines topics in areas such as Sliding mode control and Variable structure control.
Riccardo Scattolini mostly deals with Control theory, Model predictive control, Nonlinear system, Control engineering and Mathematical optimization. Control theory, Robustness, Linear system, Nonlinear control and Discrete time and continuous time are among the areas of Control theory where the researcher is concentrating his efforts. His study in Model predictive control is interdisciplinary in nature, drawing from both Stability, Control system, Robust control and Scheme.
His Nonlinear system research incorporates elements of Artificial neural network, Recurrent neural network and Optimal control. His study connects Automotive engineering and Control engineering. His Optimization problem study, which is part of a larger body of work in Mathematical optimization, is frequently linked to Noise, bridging the gap between disciplines.
Riccardo Scattolini mainly focuses on Model predictive control, Control theory, Nonlinear system, Optimization problem and Mathematical optimization. His work deals with themes such as Linear system, State, Predictive modelling, Control theory and Scheme, which intersect with Model predictive control. His Control theory research integrates issues from Control and Layer.
Riccardo Scattolini has included themes like Artificial neural network and Recurrent neural network in his Nonlinear system study. The various areas that he examines in his Optimization problem study include Distributed generation, AC power, Distributed computing and Actuator. Riccardo Scattolini combines subjects such as Function and Set with his study of Mathematical optimization.
Riccardo Scattolini mainly investigates Model predictive control, Control theory, Control, State and Stability. His Model predictive control research incorporates elements of Control theory, Linear system and Mathematical optimization. His Control theory research incorporates themes from Scheme and Microgrid.
His work carried out in the field of Control brings together such families of science as Layer and Minification. The study incorporates disciplines such as Process, Dynamical system, Artificial neural network, Property and Probabilistic logic in addition to State. His research investigates the connection between Stability and topics such as Observer that intersect with issues in Exponential stability, Nonlinear system and Recurrent neural network.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Architectures for distributed and hierarchical Model Predictive Control - A review
Journal of Process Control (2009)
Distributed model predictive control: A tutorial review and future research directions
Panagiotis D. Christofides;Riccardo Scattolini;David Muñoz de la Peña;Jinfeng Liu.
Computers & Chemical Engineering (2013)
Constrained receding-horizon predictive control
D.W. Clarke;R. Scattolini.
IEE Proceedings D Control Theory and Applications (1991)
A stabilizing model-based predictive control algorithm for nonlinear systems
L. Magni;G.De Nicolao;L. Magnani;R. Scattolini.
Stabilizing receding-horizon control of nonlinear time-varying systems
G. De Nicolao;L. Magni;R. Scattolini.
IEEE Transactions on Automatic Control (1998)
Robust model predictive control for nonlinear discrete-time systems
L. Magni;G. De Nicolao;R. Scattolini;F. Allgöwer.
International Journal of Robust and Nonlinear Control (2003)
Distributed predictive control: A non-cooperative algorithm with neighbor-to-neighbor communication for linear systems
Marcello Farina;Riccardo Scattolini.
Model Predictive Control Schemes for Consensus in Multi-Agent Systems with Single- and Double-Integrator Dynamics
G. Ferrari-Trecate;L. Galbusera;M.P.E. Marciandi;R. Scattolini.
IEEE Transactions on Automatic Control (2009)
Technical communique: Stabilizing decentralized model predictive control of nonlinear systems
L. Magni;R. Scattolini.
Stochastic linear Model Predictive Control with chance constraints – A review
Marcello Farina;Luca Giulioni;Riccardo Scattolini.
Journal of Process Control (2016)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: