Optimal Controller

Optimal controller design aims to find control strategies that minimize a cost function while satisfying constraints, a crucial problem across diverse fields like robotics and process control. Current research emphasizes data-driven approaches, employing techniques like Bayesian optimization, reinforcement learning (including actor-critic methods and model-free RL), and neural networks (including physics-informed and universal policy networks) to handle complex system dynamics and uncertainties. These advancements enable more efficient and robust control in applications ranging from legged robots navigating challenging terrains to optimizing energy systems and industrial processes, improving performance and safety.