no code implementations • 11 Sep 2023 • Johannes Autenrieb, Anuradha M. Annaswamy
Adaptive control has focused on online control of dynamic systems in the presence of parametric uncertainties, with solutions guaranteeing stability and control performance.
no code implementations • 8 May 2023 • 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.
no code implementations • 5 Jan 2023 • Emirhan Inanc, Yigit Gurses, Abdullah Habboush, Yildiray Yildiz, Anuradha M. Annaswamy
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.
no code implementations • 13 Oct 2022 • Anuradha M. Annaswamy, Vineet Jagadeesan Nair
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.
no code implementations • 11 Jun 2022 • 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, and social networks.
no code implementations • 7 Jul 2021 • 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 generation and consumption at the consumer level becomes important.
no code implementations • 13 May 2021 • Anuradha M. Annaswamy, Anubhav Guha, Yingnan Cui, Sunbochen Tang, Peter A. Fisher, Joseph E. Gaudio
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.
no code implementations • 30 Mar 2021 • 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 no PE condition.
no code implementations • 23 Mar 2021 • Spencer McDonald, Yingnan Cui, Joseph E. Gaudio, Anuradha M. Annaswamy
Gradient-descent based iterative algorithms pervade a variety of problems in estimation, prediction, learning, control, and optimization.
no code implementations • 2 Feb 2021 • Rabab Haider, David D'Achiardi, Venkatesh Venkataramanan, Anurag Srivastava, Anjan Bose, Anuradha M. Annaswamy
The increasing penetration of intermittent renewables, storage devices, and flexible loads is introducing operational challenges in distribution grids.
no code implementations • 19 Nov 2020 • José M. Moreu, Anuradha M. Annaswamy
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.
no code implementations • 23 Jun 2020 • Arnab Sarker, Peter Fisher, Joseph E. Gaudio, Anuradha M. Annaswamy
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.
no code implementations • 4 May 2020 • Joseph E. Gaudio, Anuradha M. Annaswamy, José M. Moreu, Michael A. Bolender, Travis E. Gibson
Recently, connections with variational approaches have led to the derivation of new learning algorithms with accelerated learning guarantees.
no code implementations • 10 Nov 2019 • Joseph E. Gaudio, Anuradha M. Annaswamy, Eugene Lavretsky, Michael A. Bolender
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.
no code implementations • 11 Apr 2019 • Joseph E. Gaudio, Travis E. Gibson, Anuradha M. Annaswamy, Michael A. Bolender, Eugene Lavretsky
This paper demonstrates many immediate connections between adaptive control and optimization methods commonly employed in machine learning.
no code implementations • 12 Mar 2019 • Joseph E. Gaudio, Travis E. Gibson, Anuradha M. Annaswamy, Michael A. Bolender
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.
1 code implementation • 5 Oct 2013 • Zheng Qu, Daniel Wiese, Anuradha M. Annaswamy, Eugene Lavretsky
This paper presents a method to square up a generic MIMO system that already possesses transmission zeros.
Optimization and Control