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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…
A machine-learning approach to synthesize virtual sensors for parameter-varying systems
Daniele Masti, Daniele Bernardini, Alberto Bemporad
This paper introduces a novel model-free approach to synthesize virtual sensors for the estimation of dynamical quantities that are unmeasurable at runtime but are available for de…
Piecewise linear regression and classification
Alberto Bemporad
This paper proposes a method for solving multivariate regression and classification problems using piecewise linear predictors over a polyhedral partition of the feature space. The…
A Dual Active-Set Solver for Embedded Quadratic Programming Using Recursive LDL' Updates
Daniel Arnström, Alberto Bemporad, Daniel Axehill
In this paper we present a dual active-set solver for quadratic programming which has properties suitable for use in embedded model predictive control applications. In particular,…
Exact and Heuristic Methods with Warm-start for Embedded Mixed-Integer Quadratic Programming Based on Accelerated Dual Gradient Projection
Vihangkumar V. Naik, Alberto Bemporad
Small-scale Mixed-Integer Quadratic Programming (MIQP) problems often arise in embedded control and estimation applications. Driven by the need for algorithmic simplicity to target…