The recent developments in the research area of control systems and data-driven methodologies have shown a significant shift towards enhancing precision, stability, and robustness in various applications. A notable trend is the exploration of multi-signal informativity conditions, which aim to improve model identification and control strategies by addressing the challenges of maintaining continuous excitation in control signals. This approach not only extends existing theoretical frameworks but also introduces innovative experimental design methods that can be applied both offline and online, even with limited information. Another key development is the advancement in control strategies for precise position regulation, particularly in DC servo motors, where state-feedback controllers augmented with integral action have demonstrated superior performance in terms of stability, precision, and dynamic response. Furthermore, the integration of physical activity management into the control of long-term diabetes progression represents a novel application of control-theoretical approaches to healthcare, offering a quantitative assessment of medical guidelines and robust control laws that account for inter-patient variability. Lastly, the conceptualization of counterfactuals within a control system framework introduces a physics-informed perspective to artificial intelligence, enabling a deeper understanding of the mechanisms underlying changes in system states and paving the way for the integration of machine learning models with physical knowledge.

Noteworthy Papers

• Informativity Conditions for Multiple Signals: Introduces three novel informativity conditions for multi-signal data, enhancing least-squares identification and extending Willems' fundamental lemma.
• Comparative Analysis of Control Strategies for Position Regulation in DC Servo Motors: Demonstrates the superiority of state-feedback controllers with integral action in achieving precise position control with optimal dynamic performance.
• The Intrinsic State Variable in Fundamental Lemma and Its Use in Stability Design for Data-based Control: Proposes a novel approach to stability design using the coefficient vector as an intrinsic state variable, simplifying control action construction.
• Closed-loop robust control of long-term diabetes progression via physical activity management: Develops a feedback law for physical activity management in diabetes control, showcasing robustness against initial conditions and parameter perturbations.
• A control system framework for counterfactuals: an optimization based approach: Establishes a physics-informed theoretical foundation for counterfactuals, facilitating the integration of machine learning with physical system knowledge.