Adaptive Controller

Adaptive control systems dynamically adjust their parameters to maintain desired performance despite uncertainties or changes in the environment. Current research emphasizes developing robust and efficient adaptive controllers using various techniques, including model predictive control, reinforcement learning (with both actor-critic and Q-learning methods), and deep neural networks (often incorporating dropout regularization or meta-learning for improved generalization). These advancements are crucial for applications ranging from autonomous vehicles and robotics to aerospace systems and traffic management, enabling improved performance, reliability, and adaptability in complex and unpredictable scenarios.