5 papers
Data-Driven Koopman-Enhanced Extremum Seeking for Oscillation Damping in Nonlinear Systems
Timothy I. Salsbury, Min Gyung Yu, Sayak Mukherjee
We propose a novel extremum seeking control (ESC) method that operates in a lifted Koopman state space to minimize the filtered RMS energy in the dominant subspace. The lifted repr…
Large Language Model-Based Reward Design for Deep Reinforcement Learning-Driven Autonomous Cyber Defense
Sayak Mukherjee, Samrat Chatterjee, Emilie Purvine +2
Designing rewards for autonomous cyber attack and defense learning agents in a complex, dynamic environment is a challenging task for subject matter experts. We propose a large lan…
Control Affine Hybrid Power Plant Subsystem Modeling for Supervisory Control Design
Stephen Ampleman, Himanshu Sharma, Sayak Mukherjee +1
Hybrid power plants (HPPs) combine multiple power generators (conventional/variable) and energy storage capabilities to support generation inadequacy and grid demands. This paper i…
Supervisory Control of Hybrid Power Plants Using Online Feedback Optimization: Designs and Validations with a Hybrid Co-Simulation Engine
Sayak Mukherjee, Himanshu Sharma, Wenceslao Shaw Cortez +6
This research investigates designing a supervisory feedback controller for a hybrid power plant that coordinates the wind, solar, and battery energy storage plants to meet the desi…
An Uncertainty-Aware Data-Driven Predictive Controller for Hybrid Power Plants
Manavendra Desai, Himanshu Sharma, Sayak Mukherjee +1
Given the advancements in data-driven modeling for complex engineering and scientific applications, this work utilizes a data-driven predictive control method, namely subspace pred…