Quadratic Control

Quadratic control focuses on optimizing control strategies for systems described by quadratic cost functions, aiming to find optimal control inputs that minimize these costs. Current research emphasizes distributed and adaptive control approaches, employing algorithms like policy gradient methods and Model-agnostic Meta-learning (MAML) to handle networked systems with communication constraints and model uncertainties, respectively. These advancements are significant for improving the efficiency and robustness of control systems in large-scale applications, such as robotics and smart grids, by enabling near-optimal performance even with limited information exchange or imperfect models.