Current Trends in Data-Driven Control and Robustness

Recent advancements in the field of data-driven control and robustness have shown significant progress in handling uncertainties and adversarial attacks. The focus has shifted towards developing methodologies that not only enhance the robustness of control systems against adversarial manipulations but also improve the scalability and adaptability of controllers to various system classes. Key innovations include the use of adversarial training to fortify transformers against hijacking attacks, the introduction of data-driven min-max model predictive control for LPV systems with unknown scheduling signals, and the application of bias correction and instrumental variables in direct data-driven model-reference control. These developments collectively aim to create more resilient and versatile control systems that can operate effectively under a wide range of conditions and threats.

Noteworthy Papers:

• A study on adversarial robustness in transformers highlights the effectiveness of adversarial training in enhancing robustness against hijacking attacks.
• A novel data-driven min-max MPC scheme for LPV systems demonstrates the feasibility of controlling systems with unknown scheduling signals.
• An approach using bias correction and instrumental variables in data-driven control shows promise in managing noisy data effectively.