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Found 18 papers, 7 papers with code
Online convex optimization for robust control of constrained dynamical systems
This article investigates the problem of controlling linear time-invariant systems subject to time-varying and a priori unknown cost functions, state and input constraints, and exogenous disturbances.
Solving Decision-Dependent Games by Learning from Feedback
This paper tackles the problem of solving stochastic optimization problems with a decision-dependent distribution in the setting of stochastic monotone games and when the distributional dependence is unknown.
Optimal Power Flow Pursuit via Feedback-based Safe Gradient Flow
Numerical experiments on a 93-bus distribution system and with realistic load and production profiles show a superior performance in terms of voltage regulation relative to existing methods.
Online Regulation of Dynamical Systems to Solutions of Constrained Optimization Problems
This paper considers the problem of regulating a dynamical system to equilibria that are defined as solutions of an input- and state-constrained optimization problem.
Singular Perturbation via Contraction Theory
In this paper, we provide a novel contraction-theoretic approach to analyze two-time scale systems.
Renewable-Based Charging in Green Ride-Sharing
We propose a new mechanism to promote EV charging during hours of high renewable generation, and we introduce the concept of charge request, which is issued by a power utility company.
Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems
This paper presents a system identification framework -- inspired by multi-task learning -- to estimate the dynamics of a given number of linear time-invariant (LTI) systems jointly by leveraging structural similarities across the systems.
Perception-Based Sampled-Data Optimization of Dynamical Systems
Moreover, we show that the interconnected system tracks the solution trajectory of the underlying optimization problem up to an error that depends on the approximation errors of the neural network and on the time-variability of the optimization problem; the latter originates from time-varying safety and performance objectives, input constraints, and unknown disturbances.
Online Weak-form Sparse Identification of Partial Differential Equations
This paper presents an online algorithm for identification of partial differential equations (PDEs) based on the weak-form sparse identification of nonlinear dynamics algorithm (WSINDy).
Stochastic Saddle Point Problems with Decision-Dependent Distributions
To find equilibrium points, we develop deterministic and stochastic primal-dual algorithms and demonstrate their convergence with constant step-size in the former and polynomial decay step-size schedule in the latter.
OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression
This paper presents a new regularization approach -- termed OpReg-Boost -- to boost the convergence and lessen the asymptotic error of online optimization and learning algorithms.
Online Optimization of LTI Systems Under Persistent Attacks: Stability, Tracking, and Robustness
We study the stability properties of a control system composed of a dynamical plant and a feedback controller, the latter generating control signals that can be compromised by a malicious attacker.
Optimization and Control
Online State Estimation for Time-Varying Systems
The paper investigates the problem of estimating the state of a time-varying system with a linear measurement model; in particular, the paper considers the case where the number of measurements available can be smaller than the number of states.
Optimization and Learning with Information Streams: Time-varying Algorithms and Applications
Approaches for the design of time-varying or online first-order optimization methods are discussed, with emphasis on algorithms that can handle errors in the gradient, as may arise when the gradient is estimated.
Inexact Online Proximal-gradient Method for Time-varying Convex Optimization
The online proximal-gradient method is inexact, in the sense that: (i) it relies on an approximate first-order information of the smooth component of the cost; and, (ii) the proximal operator (with respect to the non-smooth term) may be computed only up to a certain precision.
Data-based Distributionally Robust Stochastic Optimal Power Flow, Part I: Methodologies
We propose a data-based method to solve a multi-stage stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions.
Optimization and Control Systems and Control
Data-based Distributionally Robust Stochastic Optimal Power Flow, Part II: Case studies
Here, we present extensive numerical experiments in both distribution and transmission networks to illustrate the effectiveness and flexibility of the proposed methodology for balancing efficiency, constraint violation risk, and out-of-sample performance.
Optimization and Control Systems and Control
Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions.
Optimization and Control Systems and Control
