Control Strategy

Control strategy research focuses on designing and optimizing algorithms to govern the behavior of dynamic systems, aiming for efficient, robust, and accurate performance. Current efforts concentrate on developing and comparing model-free and model-based approaches, including reinforcement learning (with actor-critic and policy gradient methods), model predictive control, and neural network-based techniques, often applied within frameworks like PID controllers or Kalman filters. These advancements have significant implications across diverse fields, from robotics and autonomous vehicles to industrial process control and even disease management in agriculture, enabling improved automation, safety, and efficiency.