145 citations · 441 across the 37 of their papers we have counts for
41 papers
Accelerated Algorithms for a Class of Optimization Problems with Equality and Box Constraints
Anjali Parashar, Priyank Srivastava, Anuradha M. Annaswamy
Convex optimization with equality and inequality constraints is a ubiquitous problem in several optimization and control problems in large-scale systems. Recently there has been a…
Discrete-Time High Order Tuner With A Time-Varying Learning Rate
Yingnan Cui, Anuradha M. Annaswamy
We propose a new discrete-time online parameter estimation algorithm that combines two different aspects, one that adds momentum, and another that includes a time-varying learning…
Indirect Adaptive Optimal Control in the Presence of Input Saturation
Sunbochen Tang, Anuradha M. Annaswamy
In this paper, we propose a combined Magnitude Saturated Adaptive Control (MSAC)-Model Predictive Control (MPC) approach to linear quadratic tracking optimal control problems with…
Neural Network Adaptive Control with Long Short-Term Memory
Emirhan Inanc, Yigit Gurses, Abdullah Habboush +2
In this study, we propose a novel adaptive control architecture, which provides dramatically better transient response performance compared to conventional adaptive control methods…
Human Behavioral Models Using Utility Theory and Prospect Theory
Anuradha M. Annaswamy, Vineet Jagadeesan Nair
Several examples of Cyber-physical human systems (CPHS) include real-time decisions from humans as a necessary building block for the successful performance of the overall system.…
Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems
Rabab Haider, Anuradha M. Annaswamy
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,…
Accelerated Algorithms for a Class of Optimization Problems with Constraints
Anjali Parashar, Priyank Srivastava, Anuradha M. Annaswamy
This paper presents a framework to solve constrained optimization problems in an accelerated manner based on High-Order Tuners (HT). Our approach is based on reformulating the orig…
Discrete-Time Adaptive Control of a Class of Nonlinear Systems Using High-Order Tuners
Peter A. Fisher, Anuradha M. Annaswamy
This paper concerns the adaptive control of a class of discrete-time nonlinear systems with all states accessible. Recently, a high-order tuner algorithm was developed for the mini…
Accelerated Performance and Accelerated Learning with Discrete-Time High-Order Tuners
Yingnan Cui, Anuradha M. Annaswamy
We consider two high-order tuners that have been shown to have accelerated performance, one based on Polyak's heavy ball method and another based on Nesterov's acceleration method.…
Flattening the Duck Curve: A Case for Distributed Decision Making
Rabab Haider, Giulio Ferro, Michela Robba +1
The large penetration of renewable resources has resulted in rapidly changing net loads, resulting in the characteristic "duck curve". The resulting ramping requirements of bulk sy…
A Hierarchical Local Electricity Market for a DER-rich Grid Edge
Vineet Jagadeesan Nair, Venkatesh Venkataramanan, Rabab Haider +1
With increasing penetration of DERs in the distribution system, it is critical to design market structures that enable smooth integration of DERs. A hierarchical local electricity…
A Reactive Power Market for the Future Grid
Adam Potter, Rabab Haider, Giulio Ferro +2
As pressures to decarbonize the electricity grid increase, the grid edge is witnessing a rapid adoption of distributed and renewable generation. As a result, traditional methods fo…
A Historical Perspective of Adaptive Control and Learning
Anuradha M. Annaswamy, Alexander L. Fradkov
This article provides a historical perspective of the field of adaptive control over the past seven decades and its intersection with learning. A chronology of key events over this…
DER Forecast using Privacy Preserving Federated Learning
Venkatesh Venkataramanan, Sridevi Kaza, Anuradha M. Annaswamy
With increasing penetration of Distributed Energy Resources (DERs) in grid edge including renewable generation, flexible loads, and storage, accurate prediction of distributed gene…
Online Algorithms and Policies Using Adaptive and Machine Learning Approaches
Anuradha M. Annaswamy, Anubhav Guha, Yingnan Cui +3
This paper considers the problem of real-time control and learning in dynamic systems subjected to parametric uncertainties. We propose a combination of a Reinforcement Learning (R…
Sensitivity Analysis of Passenger Behavioral Model for Dynamic Pricing of Shared Mobility on Demand
Vineet Jagadeesan Nair, Yue Guan, Anuradha M. Annaswamy +2
This paper provides a framework to quantify the sensitivity associated with behavioral models based on Cumulative Prospect Theory (CPT). These are used to design dynamic pricing st…
Online Policies for Real-Time Control Using MRAC-RL
Anubhav Guha, Anuradha Annaswamy
In this paper, we propose the Model Reference Adaptive Control & Reinforcement Learning (MRAC-RL) approach to developing online policies for systems in which modeling errors occur…
A High-order Tuner for Accelerated Learning and Control
Spencer McDonald, Yingnan Cui, Joseph E. Gaudio +1
Gradient-descent based iterative algorithms pervade a variety of problems in estimation, prediction, learning, control, and optimization. Recently iterative algorithms based on hig…
New Algorithms for Discrete-Time Parameter Estimation
Yingnan Cui, Joseph E. Gaudio, Anuradha M. Annaswamy
We propose two algorithms for discrete-time parameter estimation, one for time-varying parameters under persistent excitation (PE) condition, another for constant parameters under…
Reinventing the Utility for DERs: A Proposal for a DSO-Centric Retail Electricity Market
Rabab Haider, David D'Achiardi, Venkatesh Venkataramanan +3
The increasing penetration of intermittent renewables, storage devices, and flexible loads is introducing operational challenges in distribution grids. The proper coordination and…
MRAC-RL: A Framework for On-Line Policy Adaptation Under Parametric Model Uncertainty
Anubhav Guha, Anuradha Annaswamy
Reinforcement learning (RL) algorithms have been successfully used to develop control policies for dynamical systems. For many such systems, these policies are trained in a simulat…
A Stable High-order Tuner for General Convex Functions
José M. Moreu, Anuradha M. Annaswamy
Iterative gradient-based algorithms have been increasingly applied for the training of a broad variety of machine learning models including large neural-nets. In particular, moment…
Transactive Control of Electric Railways Using Dynamic Market Mechanisms
David D'Achiardi, Anuradha M. Annaswamy, Sudip K. Mazumder +1
Electricity demand of electric railways is a relatively unexplored source of flexibility in demand response applications in power systems. In this paper, we propose a transactive c…
Accurate Parameter Estimation for Risk-aware Autonomous Systems
Arnab Sarker, Peter Fisher, Joseph E. Gaudio +1
Analysis and synthesis of safety-critical autonomous systems are carried out using models which are often dynamic. Two central features of these dynamic systems are parameters and…