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Found 25 papers, 5 papers with code
Linear and nonlinear system identification under $\ell_1$- and group-Lasso regularization via L-BFGS-B
In this paper, we propose an approach for identifying linear and nonlinear discrete-time state-space models, possibly under $\ell_1$- and group-Lasso regularization, based on the L-BFGS-B algorithm.
Learning disturbance models for offset-free reference tracking
This work presents a nonlinear MPC framework that guarantees asymptotic offset-free tracking of generic reference trajectories by learning a nonlinear disturbance model, which compensates for input disturbances and model-plant mismatch.
Harmonic model predictive control for tracking periodic references
Harmonic model predictive control (HMPC) is a recent model predictive control (MPC) formulation for tracking piece-wise constant references that includes a parameterized artificial harmonic reference as a decision variable, resulting in an increased performance and domain of attraction with respect to other MPC formulations.
Data-Driven Synthesis of Configuration-Constrained Robust Invariant Sets for Linear Parameter-Varying Systems
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.
Computation of safe disturbance sets using implicit RPI sets
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 as closely as possible the output constraint set, while being included in it.
Parameter Dependent Robust Control Invariant Sets for LPV Systems with Bounded Parameter Variation Rate
We present a method to synthesize parameter-dependent robust control invariant (PD-RCI) sets for LPV systems with bounded parameter variation, in which invariance is induced using PD-vertex control laws.
Self-concordant Smoothing for Large-Scale Convex Composite Optimization
We introduce a notion of self-concordant smoothing for minimizing the sum of two convex functions, one of which is smooth and the other may be nonsmooth.
Counter-example guided inductive synthesis of control Lyapunov functions for uncertain systems
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 affected by parametric uncertainty with arbitrary shape.
Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates
Optimization problems involving mixed variables, i. e., variables of numerical and categorical nature, can be challenging to solve, especially in the presence of complex constraints.
An active learning method for solving competitive multi-agent decision-making and control problems
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 external observer (or entity) can probe the agents' reactions and recursively update simple local parametric estimates of the action-reaction mappings.
Learning Critical Scenarios in Feedback Control Systems for Automated Driving
We examine the approach on the case of a feedback control system for automated driving, for which we suggest the design of the objective function expressing the criticality of scenarios.
Active Learning for Regression by Inverse Distance Weighting
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.
Training Recurrent Neural Networks by Sequential Least Squares and the Alternating Direction Method of Multipliers
This paper proposes a novel algorithm for training recurrent neural network models of nonlinear dynamical systems from an input/output training dataset.
SCORE: Approximating Curvature Information under Self-Concordant Regularization
In this paper, we propose the SCORE (self-concordant regularization) framework for unconstrained minimization problems which incorporates second-order information in the Newton-decrement framework for convex optimization.
Data-driven synthesis of Robust Invariant Sets and Controllers
This paper presents a method to identify an uncertain linear time-invariant (LTI) prediction model for tube-based Robust Model Predictive Control (RMPC).
Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filtering
We also explore the use of the algorithm in data-driven nonlinear model predictive control and its relation with disturbance models for offset-free closed-loop tracking.
Piecewise linear regression and classification
This paper proposes a method for solving multivariate regression and classification problems using piecewise linear predictors over a polyhedral partition of the feature space.
Exact and Heuristic Methods with Warm-start for Embedded Mixed-Integer Quadratic Programming Based on Accelerated Dual Gradient Projection
Moreover, in order to find an integer feasible combination of the binary variables upfront, two heuristic approaches are presented: ($i$) without using B&B, and ($ii$) using B&B with a significantly reduced number of QP relaxations.
Optimization and Control Systems and Control Systems and Control
Reduction of the Number of Variables in Parametric Constrained Least-Squares Problems
For linearly constrained least-squares problems that depend on a vector of parameters, this paper proposes techniques for reducing the number of involved optimization variables.
Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?
For all its successes, Reinforcement Learning (RL) still struggles to deliver formal guarantees on the closed-loop behavior of the learned policy.
Constrained Controller and Observer Design by Inverse Optimality
When a baseline linear controller exists that is already well tuned in the absence of constraints and MPC is introduced to enforce them, one would like to avoid altering the original linear feedback law whenever they are not active.
Efficient Calibration of Embedded MPC
Model Predictive Control (MPC) is a powerful and flexible design tool of high-performance controllers for physical systems in the presence of input and output constraints.
Systems and Control Systems and Control Optimization and Control
Active preference learning based on radial basis functions
The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences.
Global optimization via inverse distance weighting and radial basis functions
Global optimization problems whose objective function is expensive to evaluate can be solved effectively by recursively fitting a surrogate function to function samples and minimizing an acquisition function to generate new samples.
OSQP: An Operator Splitting Solver for Quadratic Programs
We present a general purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requires the solution of a quasi-definite linear system with the same coefficient matrix at almost every iteration.
Optimization and Control
