Research Index

A novel framework combining adaptive control networks with cryogenic hydrogen storage systems to create resilient, self-optimizing storage facilities. The system uses distributed agents to monitor and adjust storage conditions while defending against system anomalies and environmental disturbances.

A novel approach combining State-Dependent Riccati Equation (SDRE) control with Iterative Learning Control (ILC) for personalized cancer treatment optimization. The system uses multiple autonomous agents to adaptively adjust chemotherapy dosing based on real-time patient response data while maintaining prescribed performance boundaries.

Develop an intelligent digital twin system that combines safety-critical control theory with real-time hydrogen storage monitoring using adaptive learning algorithms. The system would integrate multiple data streams to predict and prevent hazardous conditions while optimizing storage efficiency through reinforcement learning approaches.

A novel framework combining digital twin technology, human-state-aware robotics, and safety-critical control for collaborative maintenance of hydrogen storage systems. The system adapts robot behavior based on real-time human state monitoring and safety constraints while maintaining a digital twin for training, simulation, and risk assessment.

Develop an intelligent control system that combines machine learning, human expertise, and real-time safety monitoring for hydrogen storage facilities. The system would adapt its control parameters based on both sensor data and human operator feedback, while maintaining strict safety constraints through barrier functions.

A novel framework combining large language models' reasoning capabilities with microgrid control systems to enable more intelligent and adaptive power management. The system uses self-certainty metrics and chain-of-thought reasoning to optimize microgrid operations across multiple timescales while handling uncertainties in renewable generation and demand.