Online Control Algorithm

Online control algorithms aim to design controllers that adapt in real-time to changing environments and uncertainties, optimizing system performance while meeting constraints. Current research emphasizes developing algorithms with provable performance guarantees, particularly focusing on neural networks, reinforcement learning, and recursive least squares methods for handling non-stationary systems and noisy data. These advancements are crucial for applications ranging from autonomous vehicle control and robotics to energy systems management, enabling more robust and efficient control in complex, dynamic settings.