Optimization Based Control

Optimization-based control aims to design controllers that achieve optimal performance by solving mathematical optimization problems, often incorporating sophisticated models of the system being controlled. Current research focuses on improving the efficiency and robustness of these methods, particularly through techniques like model predictive control (MPC), autotuning methods leveraging differential programming, and the integration of machine learning, including deep reinforcement learning and large language models, to handle complex tasks and uncertainties. This approach is proving valuable across diverse applications, from robotics (including bipedal locomotion and manipulation) and warehouse automation to healthcare, enabling more precise, adaptable, and efficient control systems in challenging real-world scenarios.