Dynamic Control

Dynamic control research focuses on designing and implementing systems that adapt and respond effectively to changing conditions, aiming to optimize performance and achieve specific objectives. Current research emphasizes developing robust and efficient control strategies across diverse applications, utilizing techniques like reinforcement learning (including PPO and Q-learning), model predictive control, and iterative learning control, often coupled with advanced model architectures such as hierarchical graphs and neural networks. These advancements are impacting various fields, from robotics and autonomous systems to energy management and process control, by enabling more adaptable, resilient, and efficient systems.