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Found 17 papers, 1 papers with code
Safe and Stable Adaptive Control for a Class of Dynamic Systems
Adaptive control has focused on online control of dynamic systems in the presence of parametric uncertainties, with solutions guaranteeing stability and control performance.
Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints
Convex optimization with equality and inequality constraints is a ubiquitous problem in several optimization and control problems in large-scale systems.
Neural Network Adaptive Control with Long Short-Term Memory
We also provide an analysis of the contributions of the ANN controller and LSTM network to the control input, identifying their individual roles in compensating low and high-frequency error dynamics.
Human Behavioral Models Using Utility Theory and Prospect Theory
Examples will be drawn from transportation use cases such as shared mobility to illustrate these models as well as the distinctions between Utility Theory and Prospect Theory.
Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems
Mixed integer problems are ubiquitous in decision making, from discrete device settings and design parameters, unit production, and on/off or yes/no decision in switches, routing, and social networks.
DER Forecast using Privacy Preserving Federated Learning
With increasing penetration of Distributed Energy Resources (DERs) in grid edge including renewable generation, flexible loads, and storage, accurate prediction of distributed generation and consumption at the consumer level becomes important.
Online Algorithms and Policies Using Adaptive and Machine Learning Approaches
AC-RL controllers are proposed for both classes of systems and shown to lead to online policies that guarantee stability using a high-order tuner and accommodate parametric uncertainties and magnitude limits on the input.
New Algorithms for Discrete-Time Parameter Estimation
We propose two algorithms for discrete-time parameter estimation, one for time-varying parameters under persistent excitation (PE) condition, another for constant parameters under no PE condition.
A High-order Tuner for Accelerated Learning and Control
Gradient-descent based iterative algorithms pervade a variety of problems in estimation, prediction, learning, control, and optimization.
Reinventing the Utility for DERs: A Proposal for a DSO-Centric Retail Electricity Market
The increasing penetration of intermittent renewables, storage devices, and flexible loads is introducing operational challenges in distribution grids.
A Stable High-order Tuner for General Convex Functions
Recently, a new High-order Tuner (HT) was developed for linear regression problems and shown to have 1) stability and asymptotic convergence for time-varying regressors and 2) non-asymptotic accelerated learning guarantees for constant regressors.
Accurate Parameter Estimation for Risk-aware Autonomous Systems
Experiments are provided to support all theoretical derivations, which show that the spectral lines-based approach outperforms the Gaussian noise-based method when unmodeled dynamics are present, in terms of both parameter estimation error and Regret obtained using the parameter estimates with a Linear Quadratic Regulator in feedback.
Accelerated Learning with Robustness to Adversarial Regressors
Recently, connections with variational approaches have led to the derivation of new learning algorithms with accelerated learning guarantees.
Parameter Estimation in Adaptive Control of Time-Varying Systems Under a Range of Excitation Conditions
The main feature of this algorithm is a matrix of time-varying learning rates, which enables parameter estimation error trajectories to tend exponentially fast towards a compact set whenever excitation conditions are satisfied.
Connections Between Adaptive Control and Optimization in Machine Learning
This paper demonstrates many immediate connections between adaptive control and optimization methods commonly employed in machine learning.
Provably Correct Learning Algorithms in the Presence of Time-Varying Features Using a Variational Perspective
This variational perspective includes higher order learning concepts and normalization, both of which stem from adaptive control, and allows stability to be established for dynamical machine learning problems where time-varying features are present.
Squaring-Up Method In the Presence of Transmission Zeros
This paper presents a method to square up a generic MIMO system that already possesses transmission zeros.
Optimization and Control
