16 papers
Timescale Separation Through the Lens of Operator Theory
Guido Carnevale, Nicola Bastianello, Luca Schenato +2
Timescale separation is a powerful tool for analyzing interconnected dynamical systems. Meanwhile, operator theory provides a general framework for studying the convergence of iter…
Suboptimal and Reduced-Order MPC via Timescale Separation
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
In this paper, we propose a generalized framework for the design and analysis of suboptimal and reduced-order nonlinear Model Predictive Control (MPC) architectures. The proposed f…
A Distributed Bilevel Framework for the Macroscopic Optimization of Multi-Agent Systems
Riccardo Brumali, Guido Carnevale, Sonia MartÃnez +1
In this paper, we propose a novel distributed algorithm to optimize the emergent macroscopic behavior of large-scale multi-agent systems via microscopic actions. We cast this task…
Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient
Lorenzo Sforni, Guido Carnevale, Ivano Notarnicola +1
In this paper, we investigate a data-driven framework to solve Linear Quadratic Regulator (LQR) problems when the dynamics is unknown, with the additional challenge of providing st…
Safe Control of Feedback-Interconnected Systems via Singular Perturbations
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
Control Barrier Functions (CBFs) have emerged as a powerful tool in the design of safety-critical controllers for nonlinear systems. In modern applications, complex systems often i…
Nonlinear MPC for Feedback-Interconnected Systems: a Suboptimal and Reduced-Order Model Approach
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
In this paper, we propose a suboptimal and reduced-order Model Predictive Control (MPC) architecture for discrete-time feedback-interconnected systems. The numerical MPC solver: (i…