6 citations · 16 across the 31 of their papers we have counts for
17 papers · 1 filter
Learning practically stabilizing output-feedback nonlinear controllers
Kui Xie, Pablo Krupa, Alberto Bemporad
This paper addresses the problem of learning an output-feedback surrogate controller offline that approximates a given, possibly computationally expensive, nonlinear controller-obs…
Amortized Nonlinear Model Predictive Control
Francesco Pillitteri, Alberto Bemporad
Nonlinear Model Predictive Control requires solving a constrained nonlinear program (NLP) in real-time at every sampling instant, a computational bottleneck that limits deployment…
Active Learning MPC Objective Functions from Preferences
Hasna El Hasnaouy, Pablo Krupa, Mario Zanon +1
Designing the objective function in Model Predictive Control (MPC) is challenging when performance assessment criteria are available only from human judgment. We adopt a preference…
Worst-case Nonlinear Regression with Error Bounds
Alberto Bemporad
We propose an active-learning method for nonlinear minimax regression. Given a nonlinear function that can be arbitrarily evaluated over a compact set, we fit a surrogate model, su…
Configuration-Constrained Tube MPC for Periodic Operation
Filippo Badalamenti, Jose A. Borja-Conde, Sampath Kumar Mulagaleti +3
Periodic operation often emerges as the economically optimal mode in industrial processes, particularly under varying economic or environmental conditions. This paper proposes a ro…
A Regularization and Active Learning Method for Identification of Quasi Linear Parameter Varying Systems
Sampath Kumar Mulagaleti, Alberto Bemporad
This paper proposes an active learning method for designing experiments to identify quasi-Linear Parameter-Varying (qLPV) models. Since informative experiments are costly, input si…
NashOpt - A Python Library for Computing Generalized Nash Equilibria
Alberto Bemporad
NashOpt is an open-source Python library for computing and designing generalized Nash equilibria (GNEs) in noncooperative games with shared constraints and real-valued decision var…
Solving Multiparametric Generalized Nash Equilibrium Problems and Explicit Game-Theoretic Model Predictive Control
Sophie Hall, Alberto Bemporad
We present a method for computing explicit solutions to parametric generalized Nash equilibrium (GNE) problems with convex quadratic cost functions and linear coupling and local co…
Learning the MPC objective function from human preferences
Pablo Krupa, Hasna El Hasnaouy, Mario Zanon +1
In Model Predictive Control (MPC), the objective function plays a central role in determining the closed-loop behavior of the system, and must therefore be designed to achieve the…
Online design of experiments by active learning for nonlinear system identification
Kui Xie, Alberto Bemporad
We investigate the use of active-learning (AL) strategies to generate the input excitation signal at runtime for system identification of linear and nonlinear autoregressive and st…
Efficient Configuration-Constrained Tube MPC via Variables Restriction and Template Selection
Filippo Badalamenti, Sampath Kumar Mulagaleti, Mario Eduardo Villanueva +2
Configuration-Constrained Tube Model Predictive Control (CCTMPC) offers flexibility by using a polytopic parameterization of invariant sets and the optimization of an associated ve…
Active Learning-Based Input Design for Angle-Only Initial Relative Orbit Determination
Kui Xie, Giovanni Romagnoli, Giordana Bucchioni +1
Accurate relative orbit determination is a significant challenge in modern space operations, particularly when relying only on angular measurements. The inherent observability limi…
Learning Low-Dimensional Embeddings for Black-Box Optimization
Riccardo Busetto, Manas Mejari, Marco Forgione +2
When gradient-based methods are impractical, black-box optimization (BBO) provides a valuable alternative. However, BBO often struggles with high-dimensional problems and limited t…
Online Learning of Nonlinear Parametric Models under Non-smooth Regularization using EKF and ADMM
Lapo Frascati, Alberto Bemporad
This paper proposes a novel combination of extended Kalman filtering (EKF) with the alternating direction method of multipliers (ADMM) for learning parametric nonlinear models onli…
Combined Learning of Linear Parameter-Varying Models and Robust Control Invariant Sets
Sampath Kumar Mulagaleti, Alberto Bemporad
Dynamical models identified from data are frequently employed in control system design. However, decoupling system identification from controller synthesis can result in situations…
Configuration-Constrained Tube MPC for Tracking
Filippo Badalamenti, Sampath Kumar Mulagaleti, Alberto Bemporad +2
This paper proposes a novel tube-based Model Predictive Control (MPC) framework for tracking varying setpoint references with linear systems subject to additive and multiplicative…
Learning disturbance models for offset-free reference tracking
Pablo Krupa, Mario Zanon, Alberto Bemporad
This work presents a nonlinear control framework that guarantees asymptotic offset-free tracking of generic reference trajectories by learning a nonlinear disturbance model, which…