Data Driven Optimal Control

Data Driven Optimal Control is a data-based approach aimed at optimizing control strategies through the collection and analysis of system operation data to achieve optimal performance. This method does not rely on precise mathematical models of the system but instead uses big data and machine learning technologies to dynamically adjust control parameters, thereby improving the system's response speed, stability, and efficiency. In the field of robotics, Data Driven Optimal Control can significantly enhance a robot's autonomous decision-making capabilities and environmental adaptability, boosting its performance in complex tasks.