Data-Driven Tracking Control Design With Noisy Data and its Application in DC Microgrid Control

This article focuses on data-driven analysis and dynamic feedback tracking controllers design solely utilizing noisy measured data. By leveraging data-based representations, we establish data-based stability conditions for discrete-time systems. Then, we propose the data-based method for designing zero steady-state error tracking controller gains to achieve set-point tracking with guarantees of asymptotic stability, bypassing mathematical modeling of the controlled plant. Furthermore, considering the robustness against both the noise during data acquisitions and the disturbance affecting closed-loop operations, we develop a data-based $H_\infty$ tracking control design method, ensuring that the closed-loop system satisfies the $H_\infty$ performance. Finally, we apply the presented approaches to dc–dc converters control in dc microgrids. Voltage control of a single converter and power sharing of parallel-connected dc–dc converters are implemented by simulations and hardware experiments. The results show that the dc-bus voltage keeps stability under the input voltage disturbance and the load change. Through the droop control, the currents of the converters are adjusted as expected, which confirms the effectiveness of the proposed methods.