A hierarchical robust control is developed for inverter-based resources to regulate the current, and voltage. One major problem is to mitigate with an unmeasurable current load disturbance, and parameter uncertainties. To address these challenges, first, we solve the high-level robust control problem by developing a chattering-free terminal sliding mode control to regulate the inverter current injection expeditiously. Then, a voltage control law along with a voltage observer is developed, and then at the low level a backstepping-dissipative-based control is developed to regulate the inverter filter current. 

A virtual resistance-based nonlinear control to stabilize and robustify the current layer of inverter-based resources, subject to the grid voltage disturbances, and the grid parameter uncertainties. A class of virtual resistances is proposed and analyzed using concepts from dissipative systems theory. Moreover, specific nonlinear virtual resistance-based controllers are derived, with their corresponding performance analytically bounded. The theoretical and simulation results show that the proposed nonlinear virtual resistance-based controllers significantly reduce the L2-gain of the closed-loop error system to grid voltage variation and parametric uncertainties. Significant improvements of the transient and steady-state current responses are also demonstrated over linear virtual resistance counterparts.   

This project is under reviews.

This project addresses the control challenges posed by a fault-induced uncertainty in both the dynamics and control input effectiveness of a class of hierarchical nonlinear systems in which the high-level dynamics is nonlinearly coupled with a multi-agent low-level dynamics. The high-level dynamics has a multiplicative uncertainty in the control input effectiveness and is subjected to an exogenous disturbance input. On the other hand, the low-level system is subjected to actuator faults causing a time-varying multiplicative uncertainty in the dynamical model and associated control effectiveness. Moreover, the nonlinear coupling between the high-level and the low-level dynamics makes the problem even more challenging. To address this problem, an online parameter estimation algorithm is designed, coupled with an adaptive splitting mechanism which automatically distributes the control action among low level multi-agent systems. A nonlinear L2-gain-based controller, and then a state-feedback controller are designed in the high-level, and the low-level, respectively, to recover the system from faults with high performance in the transient response, and reject the exogenous disturbance. The resulting analysis guarantees a robust tracking of the high-level reference command signal. 

Contact [sa19bk@fsu.edu] to get more information on the projects