Real World Control

Real-world control focuses on designing and implementing control systems that effectively manage complex, dynamic systems in real-world environments, often characterized by uncertainty and noise. Current research emphasizes developing robust control algorithms, including those based on reinforcement learning, model-predictive control, and Gaussian processes, often enhanced by techniques like Koopman theory for model reduction and relative entropy regularization for multi-agent systems. These advancements aim to improve the sample efficiency and generalization capabilities of control systems, leading to more reliable and adaptable automation in robotics, industrial processes, and other applications. The ultimate goal is to create controllers that are both effective and computationally feasible for deployment in diverse and unpredictable settings.