1.2k citations · 1.7k across the 52 of their papers we have counts for
57 papers
A Long-Short-Term Mixed-Integer Formulation for Highway Lane Change Planning
Rudolf Reiter, Armin Nurkanovic, Daniele Bernadini +2
This work considers the problem of optimal lane changing in a structured multi-agent road environment. A novel motion planning algorithm that can capture long-horizon dependencies…
Harmonic model predictive control for tracking sinusoidal references and its application to trajectory tracking
Pablo Krupa, Daniel Limon, Alberto Bemporad +1
Harmonic model predictive control (HMPC) is a recent model predictive control (MPC) formulation for tracking piece-wise constant references that includes a parameterized artificial…
Data-Driven Synthesis of Configuration-Constrained Robust Invariant Sets for Linear Parameter-Varying Systems
Manas Mejari, Sampath Kumar Mulagaleti, Alberto Bemporad
We present a data-driven method to synthesize robust control invariant (RCI) sets for linear parameter-varying (LPV) systems subject to unknown but bounded disturbances. A finite-l…
Computation of safe disturbance sets using implicit RPI sets
Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
Given a stable linear time-invariant (LTI) system subject to output constraints, we present a method to compute a set of disturbances such that the reachable set of outputs matches…
Parameter Dependent Robust Control Invariant Sets for LPV Systems with Bounded Parameter Variation Rate
Sampath Kumar Mulagaleti, Manas Mejari, Alberto Bemporad
Real-time measurements of the scheduling parameter of linear parameter-varying (LPV) systems enables the synthesis of robust control invariant (RCI) sets and parameter dependent co…
Self-concordant smoothing in proximal quasi-Newton algorithms for large-scale convex composite optimization
Adeyemi D. Adeoye, Alberto Bemporad
We introduce a notion of self-concordant smoothing for minimizing the sum of two convex functions, one of which is smooth and the other nonsmooth. The key highlight is a natural pr…
Specification-Guided Critical Scenario Identification for Automated Driving
Adam Molin, Edgar A. Aguilar, Dejan Ničković +3
To test automated driving systems, we present a case study for finding critical scenarios in driving environments guided by formal specifications. To that aim, we devise a framewor…
Certification of the proximal gradient method under fixed-point arithmetic for box-constrained QP problems
Pablo Krupa, Omar Inverso, Mirco Tribastone +1
In safety-critical applications that rely on the solution of an optimization problem, the certification of the optimization algorithm is of vital importance. Certification and subo…
Counter-example guided inductive synthesis of control Lyapunov functions for uncertain systems
Daniele Masti, Filippo Fabiani, Giorgio Gnecco +1
We propose a counter-example guided inductive synthesis (CEGIS) scheme for the design of control Lyapunov functions and associated state-feedback controllers for linear systems aff…
Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates
Mengjia Zhu, Alberto Bemporad
Optimization problems involving mixed variables (i.e., variables of numerical and categorical nature) can be challenging to solve, especially in the presence of mixed-variable cons…
An active learning method for solving competitive multi-agent decision-making and control problems
Filippo Fabiani, Alberto Bemporad
To identify a stationary action profile for a population of competitive agents, each executing private strategies, we introduce a novel active-learning scheme where a centralized e…
Learning Critical Scenarios in Feedback Control Systems for Automated Driving
Mengjia Zhu, Alberto Bemporad, Maximilian Kneissl +1
Testing is essential for verifying and validating control designs, especially in safety-critical applications. In particular, the control system governing an automated driving vehi…
A construction-free coordinate-descent augmented-Lagrangian method for embedded linear MPC based on ARX models
Liang Wu, Alberto Bemporad
This paper proposes a construction-free algorithm for solving linear MPC problems based on autoregressive with exogenous terms (ARX) input-output models. The solution algorithm rel…
Active Learning for Regression by Inverse Distance Weighting
Alberto Bemporad
This paper proposes an active learning (AL) algorithm to solve regression problems based on inverse-distance weighting functions for selecting the feature vectors to query. The alg…
Efficiently solving the harmonic model predictive control formulation
Pablo Krupa, Daniel Limon, Alberto Bemporad +1
Harmonic model predictive control (HMPC) is a model predictive control (MPC) formulation which displays several benefits over other MPC formulations, especially when using a small…
Training Recurrent Neural Networks by Sequential Least Squares and the Alternating Direction Method of Multipliers
Alberto Bemporad
This paper proposes a novel algorithm for training recurrent neural network models of nonlinear dynamical systems from an input/output training dataset. Arbitrary convex and twice-…
SCORE: Approximating Curvature Information under Self-Concordant Regularization
Adeyemi D. Adeoye, Alberto Bemporad
Optimization problems that include regularization functions in their objectives are regularly solved in many applications. When one seeks second-order methods for such problems, it…
Data-driven synthesis of Robust Invariant Sets and Controllers
Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
This paper presents a method to identify an uncertain linear time-invariant (LTI) prediction model for tube-based Robust Model Predictive Control (RMPC). The uncertain model is det…
Computation of Input Disturbance Sets for Constrained Output Reachability
Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
Linear models with additive unknown-but-bounded input disturbances are extensively used to model uncertainty in robust control systems design. Typically, the disturbance set is eit…
Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filtering
Alberto Bemporad
This paper investigates the use of extended Kalman filtering to train recurrent neural networks with rather general convex loss functions and regularization terms on the network pa…
A Simple and Fast Coordinate-Descent Augmented-Lagrangian Solver for Model Predictive Control
Liang Wu, Alberto Bemporad
This paper proposes a novel Coordinate-Descent Augmented-Lagrangian (CDAL) solver for linear, possibly parameter-varying, model predictive control (MPC) problems. At each iteration…
Massively parallelizable proximal algorithms for large-scale stochastic optimal control problems
Ajay K. Sampathirao, Panagiotis Patrinos, Alberto Bemporad +1
Scenario-based stochastic optimal control problems suffer from the curse of dimensionality as they can easily grow to six and seven figure sizes. First-order methods are suitable a…
C-GLISp: Preference-Based Global Optimization under Unknown Constraints with Applications to Controller Calibration
Mengjia Zhu, Dario Piga, Alberto Bemporad
Preference-based global optimization algorithms minimize an unknown objective function only based on whether the function is better, worse, or similar for given pairs of candidate…
Model Predictive Control with Environment Adaptation for Legged Locomotion
Niraj Rathod, Angelo Bratta, Michele Focchi +4
Re-planning in legged locomotion is crucial to track the desired user velocity while adapting to the terrain and rejecting external disturbances. In this work, we propose and test…