What is he best known for?
The fields of study he is best known for:
- Control theory
- Programming language
- Algorithm
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.
His most cited work include:
- Architectures for distributed and hierarchical Model Predictive Control - A review (977 citations)
- Distributed model predictive control: A tutorial review and future research directions (504 citations)
- Constrained receding-horizon predictive control (295 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Control theory (64.05%)
- Model predictive control (47.06%)
- Nonlinear system (22.55%)
What were the highlights of his more recent work (between 2017-2021)?
- Model predictive control (47.06%)
- Control theory (64.05%)
- Nonlinear system (22.55%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- A Two-Layer Stochastic Model Predictive Control Scheme for Microgrids (85 citations)
- Model Predictive Control Design for Dynamical Systems Learned by Echo State Networks (14 citations)
- A hierarchical multi-rate MPC scheme for interconnected systems (12 citations)
In his most recent research, the most cited papers focused on:
- Control theory
- Algorithm
- Programming language
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.
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